Method and system for implementing fuzzy image processing of image data

ABSTRACT

A system and method electronically image process a pixel belonging to a set of digital image data with respect to a membership of the pixel in a plurality of image classes. This process uses classification to determine a membership value for the pixel for each image classes and generates an effect tag for the pixel based on the fuzzy classification determination. The pixel is image processed based on the membership vector of the pixel. The image processing may include screening and filtering. The screening process screens the pixel by generating a screen value according to a position of the pixel in the set of digital image data; generating a screen amplitude weighting value based on the values in the membership vector for the pixel; multiplying the screen value and the screen amplitude weighting value to produce a modified screen value; and adding the modified screen value to the pixel of image data. The filtering process filters the pixel by lowpass filtering the pixel; highpass filtering the pixel; non-filtering the pixel; multiplying each filtered pixel by a gain factor based on the values in the membership vector associated with the pixel; and adding the products to produce a filtered pixel of image data.

The present invention relates generally to a system for processingdocument images, and more particularly, to an improved method of imageprocessing the document images utilizing a fuzzy logic classificationprocess.

BACKGROUND OF THE PRESENT INVENTION

In the reproduction of images from an original document or images fromvideo image data, and more particularly, to the rendering of image datarepresenting an original document that has been electronically scanned,one is faced with the limited resolution capabilities of the renderingsystem and the fact that output devices are mostly binary or requirecompression to binary for storage efficiency. This is particularlyevident when attempting to reproduce halftones, lines, and continuoustone (contone) images.

An image data processing system may be tailored so as to offset thelimited resolution capabilities of the rendering apparatus, but thistailoring is difficult due to the divergent processing needs required bydifferent types of images which may be encountered by the renderingdevice. In this respect, it should be understood that the image contentof the original document may consist of multiple image types, includinghalftones of various frequencies, continuous tones (contones), linecopy, error diffused images, etc. or a combination of any of the above,and some unknown degree of some or all of the above or additional imagetypes.

In view of the situation, optimizing the image processing system for oneimage type in an effort to offset the limitations in the resolution andthe depth capability of the rendering apparatus may not be possible,requiring a compromised choice which may not produce acceptable results.Thus, for example, where one optimizes the system for low frequencyhalftones, it is often at the expense of degraded rendering of highfrequency halftones, or of line copy, and visa versa.

To address this particular situation, "prior art" devices have utilizedautomatic image segmentation to serve as a tool to identify differentimage types or imagery. For example, in one such system, imagesegmentation was addressed by applying a function to the video, theoutput of which was used to instruct the image processing system as tothe type of image data present so that it could be processedappropriately. In particular, an auto-correlation function was appliedto the stream of pixel data to detect the existence and estimate thefrequency of halftone image data. Such a method automatically processesa stream of image pixels representing unknown combinations of high andlow frequency halftones, contones, and/or lines. The auto-correlationfunction was applied to the stream of image pixels, and for the portionsof the stream that contain high frequency halftone image data, thefunction produced a large number of closely spaced peaks in theresultant signal.

In another auto segmentation process, an auto-correlation function iscalculated for the stream of halftone image data at selected time delayswhich are predicted to be indicative of the image frequencycharacteristics, without prior thresholding. Valleys in the resultingauto-correlated function are detected to determine whether highfrequency halftone image is present.

An example of a "prior art" automatic segmentation circuit isillustrated in FIG. 15. The basic system as shown in FIG. 15 is made upof three modules. Input information stored in a data buffer 10 issimultaneously directed to an image property classifying section 20, thefirst module, and an image processing section 30, the second module. Theimage property classifying section 20, is made up of any number ofsubmodules, (e.g. auto-correlator 21 and discriminator 22), whichdetermine whether a block of image pixels stored in the data buffer 10is one type of imagery or another, (e.g. halftone, line/text, orcontone). In parallel with the image property classifying section 20,the image processing section 30 is made up of any number ofsub-processing sections, (e.g. high frequency halftone processor 31 lowfrequency halftone processor 32, line/text processor 33, or contoneprocessor 34), which perform image processing operations on the sameblock of image pixels as section 20. Each image sub-processing sectionperforms image processing operations that are adapted to improve theimage quality of a distinct class of imagery. The third module, controlsection 41, uses the information derived from the image classifyingsection 20, to control the image processing section 30.

The decision as to what class of imagery image data belongs to istypically binary in nature. For example, in a conventional imagesegmentation scheme image property classifying section 20 classifiesimage data as one of three classes of imagery, (high frequency halftone,low frequency halftone, or contone). Depending on those classification,image data is processed according to the properties of that class ofimagery, (either low pass filter and re-screening if it's a highfrequency halftone, threshold with a random threshold if it is a lowfrequency halftone, etc.). Also, assuming that the decision as to whichof the three classes of imagery image data belongs is based on a singleimage property, the peak count of the input image data, the resultingimage classification decision of the peak count image property is madeby thresholding the peak count into three classes of imagery as shown inFIG. 16.

Consequently, the control section 40 decides the type of imageprocessing the image data requires depending on the decision made by theclassification section 20. Thus, the output of classification section 20is quantized to one of three possibilities. The control section 40selects the output from one of the three image sub-processing sectionsbased upon this classification.

Based on the nature of conventional image classification systems, thedecisions being based on information gathered over a context of manypixels, effectively causing the image data to be lowpass filtered, theclassification decisions change from one class of imagery to anotherslowly and gradually causing artificially abrupt in the wrong places.This abrupt decision making, which produces a forced choice amongseveral distinct alternative choices, is a primary reason for theformation of visible artifacts in the resulting output image. Mosttransition points or thresholds are selected so that an image can beclassified as one class of imagery with a high degree of certainty;however, those classes of imagery that cannot be classified with suchcertainty have multiple transition points or a transition zone. Usingonly one point to define a transition zone results in the formation ofvisible artifacts in the resulting output image. Although it is possibleto make the transition zone narrower so that there is less chance thatan image falls into the zone, there exists limitations on how narrow thezone can be made. The narrowing of the transition zone is the decreasingof noise and/or variation in the information used to classify so as tonarrow the area over which classification is not "certain", resulting inless switching between classifications.

Moreover, the classification of real images covers a continuum from wellbelow to well above thresholds between classifications. This means thatthere are areas of an image which are, for example, just above athreshold. Variations in the gathered (lowpass filtered) information dueto "flaws" in the input video or ripple due to interactions between thearea of image being used for the classification process and periodicstructures in the input video results in areas falling below thethreshold. With discrete classification, this results in a drasticallydifferent classification, thereby resulting in artifacts in the renderedimage.

Thus, it is desireable to classify image data in a fuzzy manner, slowlysliding the classification from one classification to the other,reflecting the information that has been gathered. Artifacts in theresulting rendered image will now be soft and follow the contours of theimage, and so the artifacts will not be objectionable.

In general, the "prior art" describes the control section 40 asessentially having a switch as illustrated in FIG. 17. Since the imageprocessing steps performed for each class of imagery are differentdepending on the classification given to each block of input imagepixels, the switch or multiplexer allows data residing at the output ofthe image processor 30 to be directed to an output buffer 50 dependingon the decisions made by the imagery classifying section 20 which arereceived as signals on lines 23 and 24. This type of binary decisionmaking is rigid and results in image segmentation decisions that do notfail gracefully and consequently form visible artifacts in the outputimage.

To address this forming of visible artifacts in the rendered outputimage, is it has been proposed to utilize a probabilistic segmentationprocess to allow the image processing system to fail more gracefullywhen incorrect segmentation decisions are made. An example of such aprobabilistic segmentation system is illustrated in FIG. 11.

FIG. 11 shows a block diagram of a conventional image processing systemwhich incorporates a probabilistic classification system. As illustratedin FIG. 11, the conventional system receives input image data derivedfrom any number of sources, including a raster input scanner, a graphicsworkstation, an electronic memory, or other storage elements, etc. Ingeneral, the image processing system shown in FIG. 11 includesprobabilistic classifier 25, image processing section 30, an imageprocessing and control mixer 41.

Input image data is made available to the image processing system alongdata bus 15, which is sequentially processed in parallel byprobabilistic classifier 25 and image processing section 30.Probabilistic classifier 25 classifies the image data as a ratio of anumber of predetermined classes of imagery. The ratio is defined by aset of probability values that predict the likelihood the image data ismade up of a predetermined number of classes of imagery. Theprobabilities 27, one for each predetermined class of imagery, are inputto the image processing mixer or control unit 41 along with image outputdata from image processing section 30.

Image processing section 30 includes units 31, 32, and 34 that generateoutput data from the image data in accordance with methods unique toeach predetermined class of imagery. Subsequently, mixer 41 combines apercentage of each class of output image data from units 31, 32, and 34according to the ratio of the probabilities 27 determined by classifier25. The resulting output image data for mixer 41 is stored in outputbuffer 50 before subsequent transmission to an image output terminalsuch as a printer or display.

Initially, the stream of image pixels from an image input terminal (IIT)is fed to data buffer 10. The image data stored in buffer 10 is in rawgrey format, for example, 6 to 8 bits per pixel. A suitable block sizeis 16 pixels at 400 spots per inch, or 12 pixels at 300 spots per inch.Too large of a sample size results in the inability to properly switchclassification in narrow channels between fine structures in the image,or to switch soon enough when moving from one classification to another.An example of this problem is small text forming a title for a halftoneimage. Given a font size which is large enough to read, a good layoutpractice of leaving white space which is at least a half a line betweenthe text and the image, a one millimeter block turns out to be a goodcompromise with most documents. Thus, too large a sample size results inclassification transitions at the edge of objects to be larger than thewhitespace between the objects, resulting in inappropriateclassification and rendering.

With reference FIG. 12, the conventional probabilistic classifier 25 isshown in detail. The block of image pixels stored in buffer 10 istransmitted to a characteristic calculator 28 through data buffers 15.Calculator 28 provides an output value that characterizes a property ofthe image data transmitted from buffer 10, such as its peak count. Inone embodiment, a characteristic value is determined by calculator 28that represents the peak count of the block of image data. The peakcount is determined by counting those pixels whose values are thenon-trivial local area maximum or minimum in the block of image data.First local area maximum or minimum pixel values are selected dependingon whether the average value of all the pixels in the block of imagedata is higher or lower than the median value of the number of levels ofeach pixel.

After calculator 28 evaluates the peak count of the image data,probability classifier 29 determines three probability values 27 thatcorrespond to each image type associated with the peak count asexpressed by the characteristic function stored in memory 26. Thecharacteristic function, determined with apriori image data, representsa plurality of probability distributions that are determined using apopulation of images. Each probability distribution depicts theprobability that a block of image data is a certain type given theoccurrence of an image property, a peak count. For example, thecharacteristic function stored in memory 26 can be represented by thegraph shown in FIG. 13, which relates the probability distributions fora contone 1, low frequency halftone 2, and high frequency halftone 3 tothe occurrence of a particular image characteristic, which in thisexample is a peak count. The characteristic function stored in memory 26can be adjusted using input control 18. Using control 18, the resultingoutput image stored in buffer 50 can be altered by modifying thecharacteristic function representing the different classes of imageryevaluated by the image processing system 30.

Subsequently, probability classifier 29 determines each probabilityvalue by evaluating the probability distribution of each image typerepresented by the characteristic function stored in memory 26. Afterdetermining the probability values, classifier 29 outputs these resultsto image processing mixer or control 41.

The image processing section of FIG. 11 operates concurrently with theprobabilistic classifier 25 on the image data stored in buffer 10. Imageprocessing section 30 includes a high frequency halftone processing unit31, a low frequency halftone processing unit 32, and a contoneprocessing unit 34. Each processing unit processes all image data inaccordance with a particular image type. Each of the processing units31, 32, and 34 generates output blocks of unquantized video data.

Image processing control 41 mixes the data output blocks to form acomposite block of output image signals that is stored in output buffer50. The manner in which the output blocks are mixed is characterized bya ratio defined by the probability determined by the probabilisticclassifier 25.

FIG. 14 shows the conventional image processing mixer 41 in detail.Mixer 41 multiplies the output blocks with the probability, usingmultipliers 42, 43, 44. The resulting output from each multiplier isrepresentative of a percentage or ratio of each output block, the sum ofwhich defines a composite block of output image signals. The compositeblock of output image signals is formed by adding the output of themultipliers using adder 45 and by subsequently quantizing the sum ofadder 45 using quantizer 47. The resulting image block output byquantizer 47 is stored in output buffer 50 before subsequenttransmission for output to an image output terminal having limitedresolution or depth.

The above-described image classification system utilizes a probabilisticapproach to classify the image data. Such an approach presents problemsin that the classification of the image data is mutually exclusive, theimage data is classified as a particular type in absolute termseventhough the probability of the decision being correct is just over50%. This results in difficulties in trying to design an imageprocessing system which will process the image data without visibleartifacts in the rendered image when the decision on the image type doesnot have a high confidence.

Therefore, it is desirable to implement an image classification systemwhich provides a truer classification of the image type and the imagetypes are not necessarily mutually exclusive. Such a system wouldincorporate fuzzy logic, thereby allowing image data to be classified asbeing a member of more than one image class. This feature is critical inareas where the image goes from one image type to another. Moreover, itis desirable to implement a image processing system which takesadvantage of the fuzzy classification system.

One important component of the rendering system is digital filtering.The digital filtering process should be both efficient and low cost.Moreover, the filter design should have some non-separable and/ortime-varying characteristics so that the filter can be used in a fuzzysegmentation system. However, trying to achieve one goal or another canadversely impact the other goal. Various approaches have been devisedfor the implementation of digital filtering techniques which try tosolve minimize the adverse impacts. These techniques will be discussedbriefly below.

In one "prior art" digital filtering technique, a circuit performs aplurality of finite impulse response filtering functions. The circuitcomprises a plurality of filters, each implementing a predetermineddigital filter algorithm. A storage circuit stores coefficients and dataoperands utilizing the predetermined algorithm. An arithmetic unit iscoupled to the storage circuit for performing predetermined arithmeticoperations with selected coefficients and data operands. A sequencingcontrol device sequentially selects operands from the storage circuitfor input to the arithmetic unit.

In another "prior art" digital filtering technique, a digital signalprocessing apparatus provides high speed digital filtering. Thisapparatus includes at least two digital filters in parallel and amultiplexer for alternately outputting the outputs to the filters. In athird "prior art" device, a two-dimensional finite impulse responsefilter having a plurality of filter portions of essentially identicalconstruction are arranged in a parallel configuration. A de-multiplexerseparates an input data signal comprising consecutive digital words andsupplies each digital word in sequence to a separate filter portion.Subsequently, a multiplexer, coupled to the output of the filterportions, selectively outputs the filtered data from each filter portionin a sequence corresponding to the order of separation of the inputdata, thereby resulting in a filtered version of the original inputdata.

The three systems described above all have the limitation with respectto either speed or high cost. In view of these limitations, it has beenproposed to provide a plurality of one-dimensional transform units thatmay be selectively combined with an additional one-dimensional transformunit to produce a plurality of distinct two-dimensional filters, any oneof which is selectable on a pixel by pixel basis. Moreover, thisproposed conventional system has the added advantage of providingtwo-dimensional finite impulse response filters without employingmultiple, identically constructed two-dimensional filters arranged in aparallel fashion, thereby substantially reducing the complexity and costof the filter hardware. To get a better understanding of thisconventional system, the conventional system will be described below.

The conventional system, as illustrated in FIG. 1, includes imageprocessing module 20 which generally receives offset and gain correctedvideo through input line 22. Subsequently, the image processing module20 processes the input video data according to control signals from CPU24 to produce the output video signals on line 26. As illustrated inFIG. 1, the image processing module 20 may include an optionalsegmentation block 30 which has an associated line buffer 32,two-dimensional filters 34, and an optional one-dimensional effectsblock 36. Also included in image processing module 20 is line buffermemory 38 for storing the context of incoming scanlines.

Segmentation block 30, in conjunction with the associated scanlinebuffer 32, automatically determines those areas of the image which arerepresentative of halftone input region. Output from the segmentationblock, (video class), is used to implement subsequent image processingeffects in accordance with a type or class of video signals identifiedby the segmentation block. For example, the segmentation block mayidentify a region containing data representative of an input halftoneimage, in which case a lowpass filter would be used to removed screenpatterns, otherwise, a remaining text portion of the input video imagemay be processed with an edge enhancement filter to improve fine lineand character reproduction when thresholded.

Two-dimensional filter block 34 is intended to process the incoming,corrected video in accordance with the predetermined filteringselection. Prior to establishment of the required scanline content, theinput video bypasses the filter by using a bypass channel within thetwo-dimensional filter hardware. This bypass is necessary to avoiddelirious effects to the video stream that may result from filtering ofthe input video prior to establishing the proper context.

Subsequent to two-dimensional filtering, the optional one-dimensionaleffects block is used to alter the filtered, or possibly unfiltered,video data in accordance with selected one-dimensional video effects.One-dimensional video effects include, for example, thresholding,screening, inversion, tonal reproduction curve (TRC), pixel masking,one-dimensional scaling, and other effects which may be appliedone-dimensionally to the steam of video signals. As in thetwo-dimensional filter, the one-dimensional effects blocks also includesa bypass channel where no additional effects would be applied to thevideo, thereby enabling the received video to be passed through as anoutput video.

FIG. 2 illustrates the hardware of the two-dimensional filter of theconventional device described above. The hardware provides twoindependent finite impulse response, (FIR), or convolution filters. Eachfilter allows for the use of a center pixel adjust coefficient althoughfilter coefficients must be symmetric in both the fastscan and slowscandirections. The filter coefficients are programmable and may be set tozero to employ the use of a smaller filter.

Each filter processes a predetermined number of input scanlines at atime pixel by pixel, to calculate each output scanline. As previouslydescribed with respect to FIG. 1, input scanlines are buffered in linebuffer 38 to meet the filter input requirements. In addition, if thetwo-dimensional filter 34 is used with segmentation block 30 of FIG. 1,the filter must operate one scanline later than the segmentation block.

As illustrated in FIG. 2, input video is provided to slowscan filters102A and 102B from line buffer memory 38 of FIG. 1. Also provided tofilters 102A and 102B are the associated slowscan filter coefficient, ascontained in slowscan filter coefficient memory 104A and 104B,respectively. The slowscan filter coefficients are arranged in asymmetric fashion about a center scanline coefficient. The output ofslowscan filters 102A and 102B is directed to slowscan filter contextbuffers 106A and 106B, respectively. The output from the slowscan filtercontext buffers 106A and 106B are fed to a multiplexer 208 which selectsbetween which output is to be processed by a fastscan filter 230.

The selection by the multiplexer is controlled by the class type of thevideo data being processed which was determined by the segmentationblock 30 of FIG. 1. The fastscan filter coefficients to be utilized bythe fastscan filter 230 are received from one of a plurality of fastscanfilter coefficient buffers wherein the context of the buffers are fedinto a multiplexer 242 that selects the proper filter coefficients basedon the class or image type of the video being processed.

The above-described two-dimensional filter provides a filteringtechnique that is efficient; however, the filter is not time-varying aswould be required if the image processing system utilized a fuzzysegmentation process. Therefore, it is desirable to design a digitalfilter that is non-separable and/or time-varying so that it can readilyimplemented in a fuzzy segmentation system. Moreover, it is desirable todesign a time-varying filter that is self-timed and which is cascadableso as to enable proper filtering of the image data and providescalability.

SUMMARY OF THE PRESENT INVENTION

One aspect of the present invention is a method for electronically imageprocessing a pixel belonging to a set of digital image data with respectto a membership of the pixel in a plurality of image classes byreceiving a set of digital image data including the pixel; determining amembership value for the pixel for each image classes; generating aneffect tag for the pixel based on the membership determination; andimage processing the pixel based on the membership vector of the pixel.

Another aspect of the present invention is a system for electronicallyimage processing a pixel belonging to a set of digital image data withrespect to a membership of the pixel in a plurality of image classes.The system includes fuzzy classifying means for determining a membershipvalue for the pixel for each image classes; effect means for generatingan effect tag for the pixel based on the determination by the fuzzyclassifying means; and processing means for image processing the pixelbased on the membership vector of the pixel.

A third aspect of the present invention is a method for electronicallyscreening a pixel belonging to a set of digital image data with respectto a membership of the pixel in a plurality of image classes bydetermining a membership value for the pixel for each image classes;generating an effect tag for the pixel based on the membershipdetermination; and screening the pixel based on the values in themembership vector associated with the pixel.

A fourth aspect of the present invention is a method for electronicallyfiltering a pixel belonging to a set of digital image data with respectto a membership of the pixel in a plurality of image classes bydetermining a membership value for the pixel for each image classes;generating an effect tag for the pixel based on the membershipdetermination; and two-dimensionally filtering the pixel based on thevalues in the membership vector associated with the pixel.

A fifth aspect of the present invention is a system for electronicallyscreening a pixel belonging to a set of digital image data with respectto a membership of the pixel in a plurality of image classes. The systemincludes fuzzy classifying means for determining a membership value forthe pixel for each image classes; effect means for generating an effecttag for the pixel based on the determination by the fuzzy classifyingmeans; and screening means screening the pixel based on the values inthe membership vector associated with the pixel.

A sixth aspect of the present invention is a system for electronicallyfiltering a pixel belonging to a set of digital image data with respectto a membership of the pixel in a plurality of image classes. The systemincludes fuzzy classifying means for determining a membership value forthe pixel for each image classes; effect means for generating an effecttag for the pixel based on the determination by the fuzzy classifyingmeans; and filter means for two-dimensionally filtering the pixel basedon the values in the membership vector associated with the pixel.

Further objects and advantages of the present invention will becomeapparent from the following descriptions of the various features of thepresent invention.

BRIEF DESCRIPTION OF THE DRAWINGS

The following is a brief description of each drawing used in describingthe present invention, thus, the drawings are being presented forillustrative purposes only and should not be limitative of the scope ofthe present invention, wherein:

FIG. 1 is a schematic illustration of a conventional image processinghardware module incorporating a two-dimensional filter;

FIG. 2 is a block diagram illustrating the reduced hardware componentset used in the conventional image processing two-dimensional filter ofFIG. 1;

FIG. 3 is a block diagram illustrating a non-separable two-dimensionalfilter according to one embodiment of the present invention;

FIG. 4 is a block diagram illustrating an image processing systemutilizing the two-dimensional filter of FIG. 3;

FIG. 5 is a block diagram illustrating a self timed non-separabletwo-dimensional filter according to a preferred embodiment of thepresent invention;

FIG. 6 is a timing diagram illustrating the sequence of events carriedout by the self timed non-separable two-dimensional filter of FIG. 5;

FIG. 7 is a block diagram illustrating a cascadable filter according toa preferred embodiment of the present invention;

FIG. 8 is a block diagram illustrating a conventional filter;

FIG. 9 is a block diagram illustrating the formation of a single filterutilizing two cascadable filters;

FIG. 10 is a block diagram illustrating a cascadable fastscan filter;

FIG. 11 is a block diagram illustrating a conventional image processingsystem incorporating probabilistic segmentation;

FIG. 12 is a block diagram detailing the probabilistic segmentor shownin FIG. 11;

FIG. 13 shows an example of a characteristic function of the imageproperty, peak count;

FIG. 14 is a block diagram illustrating in detail the image processingmixer shown in FIG. 11;

FIG. 15 is a block diagram illustrating a "prior art" image processingsystem;

FIG. 16 shows the thresholding technique used by the "prior art" todetermine an appropriate image processing technique for images havingdifferent image types;

FIG. 17 is a block diagram illustrating in detail the "prior art" imageprocessing control circuit shown in FIG. 15;

FIG. 18 is a block diagram of a digital filtering system incorporatingthe fuzzy classification process of the present invention;

FIG. 19 is a block diagram illustrating a screening system incorporatingthe fuzzy classification process of the present invention;

FIG. 20 is a block diagram illustrating a more detailed version of thesystem illustrated in FIG. 19;

FIG. 21 illustrates a block diagram showing a chip having a plurality ofcascadable filters; and

FIG. 22 is a flowchart showing a fuzzy classification process accordingto the concepts of the present invention.

DETAILED DESCRIPTION OF THE PRESENT INVENTION

The following will be a detailed description of the drawings illustratedin the present invention. In this description, the terms "image data" or"pixels" in the form of video image signals, which may be either analogor digital voltage representations of an image, indicate arepresentation of an image provided from a suitable source. For example,the image signals may be obtained through line by line scanning of animage bearing the original by one or more photosensitive elements, suchas a multiple photosite array of charge couple devices commonly referredto as CCDs. Line by line scanning of an image bearing the original forthe duration of image data is well known and does not form a part of thepresent invention.

Image data may also be derived by a computer workstation program inaccordance with document creation application software or from a datastorage device. In content, the original video image signals may becomposed entirely of a single image component such as lines, text, lowfrequency halftones, high frequency halftones, contones, or anycombination thereof.

The following description also includes references to slowscan andfastscan digital image data when discussing the directionality oftwo-dimensional filtering architecture. For purposes of clarification,fastscan data is intended to refer to individual pixels located in asuccession along a raster of image information, while slowscan datarefers to data derived from a common raster position across multiplerasters or scanlines.

As an example, slowscan data would be used to describe signals capturedfrom a plurality of elements along a linear photosensitive array asarray is moved relative to a document. On the other hand, fastscan datawould refer to the sequential signals collected along the length of thelinear photosensitive array during a single exposure period which isalso commonly referred to as a raster of data.

Moreover, in describing the present invention, it is assumed that thevideo signal has a value in a range between 0 and 255. However, anyrange from the video signal can be utilized in conjunction with thepresent invention. Furthermore, in the following description, the term"grey level" will be used to describe both black and white and colorapplications.

Furthermore, in describing the present invention, the term "pixel" willbe utilized. This term may refer to an electrical, (or optical, if fiberoptics are used), signal which represents the physical measurableoptical properties at a physical definable area on a receiving medium.The receiving medium can be any tangible document, photoreceptor, ormarking material transfer medium.

Moreover, the term "pixel" may refer to an electrical, (or optical, iffiber optics are used), signal which represents the physicallymeasurable optical properties at a physically definable area on thedisplay medium. A plurality of the physically definable areas for bothsituations represent the physically measurable optical properties of anentire physical image to be rendered by either a material markingdevice, electrically or magnetic is marking device, or optical displaydevice.

Lastly, the term "pixel" may refer to an electrical, (or optical, iffiber optics are used), signal which represents physical opticalproperty data generated from a signal photosensor cell when scanning aphysical image so as to convert the physical optical properties of thephysical image to an electronic or electrical representation. In otherwords, in this situation, a pixel is an electrical, (or optical),representation of the physical optical properties of a physical imagemeasured at a physical definable area on a optical sensor.

Many of the documents produced today are compound documents in that thedocuments are composed of several different sub-images that are ofdifferent image types or image classes. Some of the common types aretext, photos (contones), and halftones. One reason for the increasedappearance of compound documents is the widespread use of commerciallyavailable word processing and desktop publishing software that is ableto generate them.

As is well known, different types of images require different processingin order to provide optimal image quality. Conventionally, toautomatically choose the best processing for different areas of animage, each area is classified into one of several pre-defined classesto determine how to render that part of the image. This image type orimage class information can then be used to determine the appropriateprocessing required to obtain a good rendition of the image whenprinting, to choose a method of image compression, to determine ifoptical character recognition would be useful, etc.

However, as noted previously, the classification process should not beso crisp so as to avoid problems when the input image is not verysimilar to any of the classes, or the input images properties straddlethe border between two classes.

For example, if a particular action is taken based upon a single classidentification because the classes are mutually exclusive, it may createundesirable results for a non-prototype image. This is seen whenrendering images for printing on a xerographic printer. Theclassification of the image can cause output artifacts such as when ahalftone image is classified as a contone image.

Another type of problem is that adjacent areas of the image may beclassified differently due to small variations in the image. This iscalled class switching. If this information is used for imageenhancement and printing, the output may have objectionable artifactsdue to local variations. Examples of these objectionable artifacts aregrainy image outputs.

To eliminate the above described problems, an image classificationsystem which utilizes a fuzzy membership into each category or class canbe used. In other words, the classes in a fuzzy classification systemare not mutually exclusive, thereby eliminating problems with classswitching and also allowing those areas to have processing differentthan that of any of the other pre-defined classes; i.e., the output canchoose between a continuum of possible image processing techniques.

In standard classification techniques, each area has one class assignedto it. In the fuzzy implementation of the present invention, each areahas a classification vector assigned to it. Every element of theclassification vector has a membership value associated with each of thepre-defined prototype classes.

Similar to the creation of crisp classifiers, a set of heuristic rulesare used to determine the form of the classifier. The following is anexample of how heuristic rules are used to create a fuzzy classifier,according to the concepts of the present invention.

For illustrative purposes, an example of a two class non-fuzzy system isdiscussed. In this example, the system only classifies a particularregion as either contone (i.e., grey pictorial) or text. An image may beconsidered text if there are a lot of edges and most pixels are black orwhite. If this is not true, the picture is considered contone.

In order to determine edges, a variable relating to the Laplacian of theimage data at every point (pixel) is used. A typical implementation forthis type of segmentation may be if the summation of the squares of theLaplacian at every pixel in the subblock is greater than a predeterminedsummation threshold and the sum of the percentage of pixels with greyvalue less than a black threshold value and the percentage of pixelswith grey value greater than a white threshold value is greater than apredetermined bi-modal threshold, the image is text, else the image iscontone.

In this example, since the parameters are device dependent, tests, whichare known to those skilled in the art, would be run to determine thevalues of all of the parameters; the percentage of pixels with greyvalue less than a black threshold value, the percentage of pixels withgrey value greater than a white threshold value, the summationthreshold, and the bi-modal threshold before executing the segmentationroutine. Note that only one class can be chosen, either text or contone.

In order to implement a fuzzy classifier, according to the concepts ofthe present invention, several modifications must be made to the abovedescribed heuristic rules. As described above, there exists a singlerule defining only text. If the condition for text is "not true,"contone is chosen. In the fuzzy system of the present invention, contonemust have it's own rule, since the text membership rule is not anabsolute truth, but a relative truth that the classification is true.

Moreover, even providing a rule for contones will not satisfy theexcluded middle law; therefore, a third "other" class must be added tosatisfy the constraints of fuzzy logic. Without the "other" class, itwould be possible to have membership of the image in all classes be verysmall. Thus, the "other" class creates a lower bound of a half (0.5) forthe minimum membership in any given class. A minimum magnitude for themaximum membership assures that all actions/decisions made using therelative membership values are not extremely sensitive to the classmemberships, which they would be if membership in all classes was small,thereby making the fuzzy classification more robust.

In the fuzzy classification scheme of the present invention, the pixelor unit of image data has a membership in each of the three classes;text, image and "other." In other words, the pixel is no longerconsidered to be an element of just one of the mutually exclusiveclasses. However, if the determination for one class reach absolutecertainty; i.e. the membership in a single class is 1 and the otherclasses is zero; the fuzzy system does generate values which wouldrepresent a crisp system.

In view of this non-exclusivity characteristic of the pixel imagemembership, the membership of the pixel is represented by a membershipvector, V_(i), whose entries correspond to the membership of the pixel(image element) in each of the classes. Note, typically, there are noconstraints on this vector other than all of it's elements must begreater than or equal to 0 and less than or equal to 1. However, sincethe fuzzy classification rules of the present invention have been setupwith a third "other" class, at least one of the elements of the vectormust be greater than or equal to 0.5 and less than or equal to 1.

Using the two class example above, the fuzzy classification rules wouldbe the following. If the summation of the squares of the Laplacian atevery pixel in the subblock is greater than a predetermined summationthreshold and the sum of the percentage of pixels with grey value lessthan a black threshold value and the percentage of pixels with greyvalue greater than a white threshold value is greater than apredetermined bi-modal threshold, the pixel would be assigned amembership value for the "text" class which is the minimal valueassociated with each of the conditional statements.

To better understand this concept, the following brief explanation offuzzy logic will be provided. In fuzzy logic, unlike Boolean logic, theresults of the conditional statements do not generate either absolutetrue or absolute false, but a value corresponding to the amount of theresulting statement which is true. This result is due to the fact thatthe conditional statements are also not absolute.

For example, in the above described rule, from testing, it may bedetermined that the midpoint (predetermined target condition value) ofthe fuzzy Laplacian summation condition should be 50. The midpointrepresents maximum uncertainty as to whether the value 50, in thisexample, is a member of the class Large Laplacian. Moreover, fromtesting, it is determined that, with absolute certainty, a pixel ismember of the Large Laplacian (membership equals 1.0) if the summationis equal to or greater than 75 (predetermined absolute condition value)and it is determined that, with absolute certainty, a pixel is not amember of the Large Laplacian (membership equals 0.0) if the summationis equal to or less than 25 (predetermined absolute condition value).Fuzzy logic allows the classifier to assign 0.5 (conditional value) to aresult where the summation is 50 and linearly extrapolate to theassigned values (conditional values) to 1 and 0.0 for the values 75 and25, respectively; i.e., value 55 would be assigned a membership value of0.6. Note that these values are device dependent, and thus, the midpointand the range needs to be determined for each individual device.

Furthermore, from testing, it may be determined that the midpoint of theclass bi-modal should be 80; with absolute certainty, a pixel is in themembership if the percentage sum is equal to or greater than 90; and,with absolute certainty, a pixel is not in the membership if thepercentage sum is equal to or less than 70. Fuzzy logic allows theclassifier to assign 0.5 to a result where the sum value is 80 andlinearly extrapolate to the assigned values to 1 and 0.0 for the values90 and 70, respectively; i.e., value 85 would be assigned a membershipvalue of 0.75. Note that these values are device dependent, and thus,the midpoint and the range needs to be determined for each individualdevice.

To further explain the fuzzy technique, it is assumed that themembership values for each conditional statement are 0.5 and 0.33,respectively. In this scenario, the membership value for the pixel forthe class text would be 0.33 because fuzzy logic treats "anded"statements as determining the minimal value for all the conditions andassigning the minimal value to the membership value.

Using the contone rule of if the summation of the squares of theLaplacian at every pixel in the subblock is less than a predeterminedsummation threshold and the sum of the percentage of pixels with greyvalue less than a black threshold value and the percentage of pixelswith grey value greater than a white threshold value is less than apredetermined bi-modal threshold, the image is "contone," eachconditional statement will be discussed in fuzzy logic terms.

For example, in the above described rule, from testing, it may bedetermined that the midpoint of the fuzzy Laplacian summation conditionshould be 50. Moreover, from testing, it is determined that, withabsolute certainty, a pixel is in the membership if the summation isequal to or less than 25 and it is determined that, with absolutecertainty, a pixel is not in the membership if the summation is equal toor greater than 75. Fuzzy logic allows the classifier to assign 0.5 to aresult where the summation is 50 and linearly extrapolate to theassigned values to 1 and 0.0 for the values 25 and 75, respectively;i.e., value 55 would be assigned a membership value of 0.4.

Furthermore, from testing, it may be determined that the midpoint of thefuzzy bi-modal condition should be 80; with absolute certainty, a pixelis in the membership if the sum is equal to or less than 70; and, withabsolute certainty, a pixel is not in the membership if the sum is equalto greater 90. Fuzzy logic allows the classifier to assign 0.5 to aresult where the percentage value is 80 and linearly extrapolate to theassigned values to 1 and 0.0 for the values 70 and 90, respectively;i.e., value 85 would be assigned a membership value of 0.25.

To further explain the fuzzy technique, it is assumed that themembership values for each conditional statement are 0.75 and 0.8,respectively. In this scenario, the membership value for the pixel forthe class text would be 0.75 because fuzzy logic treats "anded"statements as determining the minimal value for all the conditions andassigning the minimal value to the membership value.

Lastly, the fuzzy rules states that if image is neither "text" or"contone," the image is "other." This last rule, which defines the"other" class, can be represented mathematically as μ_(other)(image)=min(1-μ_(text) (image), 1-μ_(contone) (image)) where μ_(X) (Y)is the membership of Y in the class X. Note that if μ_(text) (image),and μ_(contone) (image) are smaller than 0.5 then μ_(other) (image) willbe greater than 0.5 (as stated earlier). In the example, given above,μ_(text) (image) is equal to 0.33 and μ_(contone) (image) is equal to0.75, thus, μ_(other) (image) would be equal to 0.25, with the resultingmembership vector being 0.33 0.75 0.25!. Note the element values of thevector need not add up to 1.

The predicate of each the rules described above is extended to a fuzzytruth instead of an absolute truth to provide the element value for themembership vector. Thus, in order to make the inequality "Y is >X" afuzzy truth, a membership function is defined for ">X". Similarly, afuzzy membership rule can be defined for <X (very often, the membershipin (<X) is equal to not (>X):(1-membership of (>X)).

For simplicity in implementation, the membership in (>X) is defined asfollows: ##EQU1##

The value of ΔX determines the level of fuzzification of the class; ifΔX is extremely small, then the definition reduces to the crispdefinition of greater than. It is further noted that although thefuzzification has been described as a linear relationship, the functiondescribing the values between the end points and the mid point may beany type of function. Moreover, the midpoint could represent absolutecertainty in the class and have a membership value of 1 and theendpoints represent absolute certainty of non-membership such that themembership values would graphically form a triangle with the midpointbeing the peak.

Returning to the multiple "If's" in the above rules, the membership ofimage in the class text is equal to the fuzzy value of the predicate,μ_(text) (image)=min(μ_(>Slp) Threshold (Σlp²), μ_(>Bimodal) Treshold(White+Black)).

To expand the concepts of the present invention to the processing ofimages on a typical xerographic laser printer requires separating imagesinto several classes; for example, white, black, edge, pictorial, lowfrequency halftone, mid frequency halftone, high frequency halftone andother, etc. The classes white, black, and pictorial are subclasses ofthe set "contone" and low frequency halftone, mid frequency halftone,high frequency halftone are subclasses of the set "halftone."

In a preferred embodiment of the present invention, the deterministicvalues for determining membership are as follows:

BLACK₋₋ %=the percentage of pixels with grey value less than a blackthreshold value;

WHITE₋₋ %=the percentage of pixels with grey value greater than a whitethreshold value;

Sij=Sum of the absolute values of the Laplacians in a window around thepixel being classified;

Range=Max grey Level--Min grey level inside a window around the pixelbeing classified; and

Freq=Measurement of local 2-D frequency around the pixel beingclassified.

To determine the membership value in a particular class, these valuesare compared to a variety of predetermined thresholds in a similarmanner as described above with respect to the three class system. FIG.22 illustrates a flowchart showing the fuzzy classification process. Asillustrated in FIG. 22, step S100 determines classification vectormembership values for classification types except for the "other" class.An example of the rules to determine these values will be explained inmore detail below. After the classification vector membership values forthe classification types except for the "other" class are determined,step S102 determines the classification vector membership value for the"other" class based on the classification types determined in step S100.The classification types and the "other" class classification vectormembership values are used to generate the classification vector, whichis used in step S106 to process the image data. The various classes in apreferred embodiment of the present invention are demonstrated by thefollowing rules:

If (Sij is >SIJ₋₋ HALFTONE and RANGE is >RANGE₋₋ HALFTONE and FREQ is>FREQ₋₋ HALFTONE), then Cl is HALFTONE;

If (Sij is >SIJ₋₋ EDGE and RANGE is >RANGE₋₋ EDGE and FREQ is <FREQ₋₋HALFTONE), then pixel is EDGE;

If (Sij is <SIJ₋₋ HALFTONE and FREQ is <FREQ₋₋ HALFTONE), then Cl isCONTONE;

If (Cl is CONTONE and BLACK₋₋ % is >BLACK₋₋ THRESHOLD), then pixel isBLACK;

If (Cl is CONTONE and WHITE₋₋ % is >WHITE₋₋ THRESHOLD), then pixel isWHITE;

If (Cl is CONTONE and BLACK₋₋ % is <BLACK₋₋ THRESHOLD and WHITE₋₋ % is>WHITE₋₋ THRESHOLD), then pixel is PICTORIAL;

If (Cl is HALFTONE and FREQ is <LOW₋₋ FREQ), then pixel is LOW₋₋ FREQ₋₋HALFTONE;

If (Cl is HALFTONE and FREQ is >LOW₋₋ FREQ and FREQ is <HIGH₋₋ FREQ),then pixel IS MID₋₋ FREQ₋₋ HALFTONE; and

If (Cl is HALFTONE and FREQ is >HIGH₋₋ FREQ), then pixel is HIGH₋₋FREQ₋₋ HALFTONE; and

If (pixel is not BLACK and pixel is not WHITE and pixel is not PICTORIALand pixel is not EDGE and pixel is not LOW₋₋ FREQ₋₋ HALFTONE and pixelis NOT MID₋₋ FREQ₋₋ HALFTONE and pixel is not HIGH₋₋ FREQ₋₋ HALFTONE),then pixel is OTHER.

The predicate of each the rules described above is extended to a fuzzytruth instead of an absolute truth to provide the element value for themembership vector. Thus, in order to make the inequality "Y is >X" afuzzy truth, a membership function is defined for ">X". Simiarly, afuzzy membership rule can be defined for <X (very often, the membershipin (<X) is equal to not (>X): (1-membership of (>X)).

For simplicity in implementation, the membership in (>X) is againdefined as follows: ##EQU2##

The value of ΔX determines the level of fuzzification of the class; ifΔX is extremely small, then the definition reduces to the crispdefinition of greater than. It is further noted that although thefuzzification has been described as a linear relationship, the functiondescribing the values between the end points and the mid point may beany type of function. Moreover, the midpoint could represent absolutecertainty in the class and have a membership value of 1 and theendpoints represent absolute certainty of non-membership such that themembership values would graphically form a triangle with the midpointbeing the peak. This type of class could be used for a membershipfunction of the class "=X".

Returning to the multiple "If's" in the above rules, the membershipvalue of image in the class "edge" would be equal to the fuzzy value ofthe predicate, μ_(edge) (image)=min(μ_(>Slp) Threshold (Σlp), μ_(>Range)Threshold (Max_(grey) Min_(grey)), μ_(<Freq) Threshold (2D Freq)); themembership value of image in the class "black" would be equal to thefuzzy value of the predicate, μ_(black) (image)=min(μ_(<Slp) Threshold(Σlp), μ_(<Freq) Threshold (2D Freq), μ_(>Black) Threshold (% of blackpixels)); the membership value of image in the class "white" would beequal to the fuzzy value of the predicate, μ_(white)(image)=min(μ_(<Slp) Threshold (ΣIp), μ_(<Freq) Threshold (2D Freq),μ_(<White) Threshold (% of white pixels)); the membership value of imagein the class "pictorial" would be equal to the fuzzy value of thepredicate, μ_(pictorial) (image)=min(μ_(<Slp) Threshold (Σlp), μ_(<Freq)Threshold (2D Freq), μ_(<Black) Threshold (% of black pixels),μ_(<White) Threshold (% of white pixels)); the membership value of imagein the class "low frequency halftone" would be equal to the fuzzy valueof the predicate, μ_(lowfreqhalf) (image)=min(μ_(>Slp) Threshold (Σlp),μ_(>Range) Threshold (Max_(grey) -Min_(grey)), μ_(>Freq) Threshold (2DFreq), μ_(<LowFreq) Threshold (2D Freq)); the membership value of imagein the class "mid frequency halftone" would be equal to the fuzzy valueof the predicate, μ_(midfreqhalf) (image)=min(μ_(>Slp) Threshold (Σlp),μ_(>Range) Threshold (Max_(grey) -Min_(grey)), μ_(>Freq) Threshold (2DFreq), μ_(>LowFreq) Threshold (2D Freq), μ_(<HighFreq) Threshold (2DFreq)); and the membership value of image in the class "high frequencyhalftone" would be equal to the fuzzy value of the predicate,μ_(highfreqhalf) (image)=min(μ_(>Slp) Threshold (Σlp), μ_(>Range)Threshold (Max_(grey) -Min_(grey)), μ_(>Freq) Threshold (2D Freq),μ_(>HighFreq) Threshold (2D Freq)).

To implement the fuzzy segmentation process for the two image classsituation, image data received from the image source is divided intoblocks of pixels. The fuzzy image classifier then, to determine amembership value for a particular image type, calculates the summationof the squares of the Laplacians in a window around the pixel beingclassified. Moreover, the fuzzy classifier calculates the percentage ofpixels that have a grey value less than a predetermined black value anddetermines the percentage of pixels within the block which have a greyvalue greater than a predetermined white value.

After calculating this information, the fuzzy classifier determines theconditional value for the condition relating to the summation of thesquares of the Laplacian of every pixel in the block being greater thana Laplacian threshold and the conditional value for the conditionrelating to the sum of the percentage of pixels with a grey valuegreater than the predetermined black value and the percentage of thepixels with a grey value greater than the predetermined white value isgreater than a bi-modal threshold value. Upon determining these twoconditional values, the fuzzy classifier determines the minimal valueand assigns this value as the membership value for the pixel in the"text" class.

The fuzzy classifier then determines the conditional value for thecondition relating to the summation of the squares of the Laplacian ofevery pixel in the block is less than a Laplacian threshold value andthe conditional value for the condition relating to the summation of thepercentage of the pixels with a grey value less than a predeterminedblack value and the percentage of the pixels with a grey value greaterthan the predetermined white value is less the bi-modal threshold value.Upon determining these two conditional values, the fuzzy classifierdetermines the minimal value and assigns this value as the membershipvalue for the pixel in the "contone" class.

The fuzzy classifier thereafter determines the membership value for thepixel in the "other" class by determining the minimal value between1--the "text" membership value and 1--the "contone" membership value andassigns this value as the membership value for the pixel in the "other"class.

In this process, the pixels of image data received by the fuzzyclassifier are assigned membership values for the three possible fuzzyclassifications, "text," "contone," or "other." As noted above, theutilization of the "other" class is necessary in order to avoid havingmembership of the image in all classes to be very small.

An example of implementing fuzzy classification for a laser xerographicprinter, will now be discussed, more specifically, a process, carriedout by a fuzzy classifier, to assign membership values to the pixels ofimage data for eight possible types or classes.

As with the process described above, the process begins by dividing thepixels of image data into blocks of pixels. Thereafter, each block ofpixels is analyzed to determine the sum of the absolute values of theLaplacians in a predetermined window around the pixel being presentlyanalyzed; to determine a range value which is equal to the maximum greylevel minus the minimum grey level inside the predetermined windowaround the pixel being presently analyzed; to determine a frequencyvalue which is equal to the measurement of the local two-dimensionalfrequency around the pixel being presently analyzed; to determine ablack value which is equal to the percentage of pixels that have a greyvalue less than a predetermined black value; and to determine a whitevalue which is equal to the percentage of pixels within the windowhaving a grey value greater than a predetermined white value.

Once these various values are determined, the fuzzy classifier beginsthe assigning of the membership values. The fuzzy classifier determinesthe conditional value for the condition relating to the sum of theabsolute values of the Laplacian in the predetermined window around thepixel being presently analyzed being greater than a halftone threshold,the conditional value for the condition relating to the range valuebeing greater than a range halftone threshold, and the conditional valuefor the condition relating to the frequency value being greater than afrequency halftone threshold. Upon determining these three conditionalvalues, the fuzzy classifier determines the minimal value and assignsthis value as the membership value for the pixel in the "halftone"class.

Then, the fuzzy classifier determines the conditional value for thecondition relating to the summation of the absolute values of theLaplacians in the predetermined window around the pixel being presentlyanalyzed being greater than an edge threshold, the conditional value forthe condition relating to the range value being greater than a rangeedge threshold, and the conditional value for the condition relating tothe frequency value being less than the frequency halftone threshold.Upon determining these three conditional values, the fuzzy classifierdetermines the minimal value and assigns this value as the membershipvalue for the pixel in the "edge" class.

The fuzzy classifier thereafter determines the conditional value for thecondition relating to the sum of the absolute values of the Laplaciansin the predetermined window around the pixel being presently analyzedbeing less than the halftone threshold and the conditional value for thecondition relating to the frequency value being less than the frequencyhalftone threshold. Upon determining these two conditional values, thefuzzy classifier determines the minimal value and assigns this value asthe membership value for the pixel in the "contone" class.

The fuzzy classifier then determines the conditional value for thecondition relating to the pixel being a contone image and theconditional value for the condition relating to the black value beinggreater than a black threshold value. Upon determining these twoconditional values, the fuzzy classifier determines the minimal valueand assigns this value as the membership value for the pixel in the"black" class.

Subsequently, the fuzzy classifier determines the conditional value forthe condition relating to the pixel being a contone image and theconditional value for the condition relating to the white value beinggreater than the predetermined white threshold. Upon determining thesetwo conditional values, the fuzzy classifier determines the minimalvalue and assigns this value as the membership value for the pixel inthe "white" class.

The fuzzy classifier determines the conditional value for the conditionrelating to the pixel being a halftone image and the conditional valuefor the condition relating to the frequency value being less than a lowfrequency threshold value. Upon determining these two conditionalvalues, the fuzzy classifier determines the minimal value and assignsthis value as the membership value for the pixel in the "low frequencyhalftone" class. Thereafter, the fuzzy classifier determines theconditional value for the condition relating to the pixel being ahalftone image, the conditional value for the condition relating to thefrequency value being greater than the low frequency threshold value,and the conditional value for the condition relating to the frequencyvalue being less than a high frequency threshold value. Upon determiningthese three conditional values, the fuzzy classifier determines theminimal value and assigns this value as the membership value for thepixel in the "mid frequency halftone" class.

The fuzzy classifier determines the conditional value for the conditionrelating to the pixel being a halftone image, the conditional value forthe condition relating to the frequency value being greater than thehigh frequency threshold value. Upon determining these two conditionalvalues, the fuzzy classifier determines the minimal value and assignsthis value as the membership value for the pixel in the "high frequencyhalftone" class.

Lastly, the fuzzy classifier determines the membership value for thepixel in the "other" class by determining the minimal value between1--the "edge" membership value, 1--the "black" membership value, 1--the"white" membership value, 1--the "pictorial" membership value, 1--the"low frequency halftone" membership value, 1--the "mid frequencyhalftone" membership value, and 1--the "high frequency halftone"membership value and assigns this value as the membership value for thepixel in the "other" class.

By utilizing these processes, the fuzzy image classifier can eliminateproblems with class switching in areas between two or more pre-definedtypes. In other words, these processes implement a fuzzy classificationscheme which allows the defining of various image types or classes tofail gracefully. Moreover, by implementing a fuzzy classificationprocess, the fuzzy process allows the fuzzy memberships to haveenhancement, different than any of the pre-defined classes. Morespecifically, the image processing system can choose between a continuumof possible processing operations to produce an output.

The present invention also allows the determination of output parameterssuch as filter coefficients and screening level given a fuzzyclassification vector and a corresponding desired output for eachprototype class. The present invention can also accommodate output typesthat must satisfy certain constraints (such as the mean gain of thefilter) through the use of the "other" class. The fuzzy image processinggreatly attenuates switching noise common in crisp decision imagesegmentation processes.

Processing image data for laser printer reproduction requires theimplementation of two separate actions: image processing to enhance,through manipulation, which does not result in loss of information, suchas filtering and TRC translation; and a lossy conversion of theresultant image to a representation accepted by a print engine, normallya reduction in the number of bits per pixel through either applicationof a screen or error diffusion. By utilizing a fuzzy classificationscheme, the present invention can easily unify the processing implied byall of the partial memberships into a single processing action.

For example, each of the classes, defined using fuzzy classification, isprovided with an ideal processing scheme. This ideal image processingscheme would represent the image processing techniques used if themembership in that class was one (1.0) and all other classes had amembership of zero (0.0). The fuzzy image classification process,described above, provides the mechanism for determining the output for agiven fuzzy membership because the output is a vector. Thus, fuzzyclassification can be used to choose each element of the vectorindependently, thereby, as noted above, providing a continuum ofpossible image processing operations. However, this continuum is notdirectly conducive to determining screening operations and determiningfilter coefficients. More specifically, a screening operation, by itsnature, is discrete; either a screen is applied or it isn't. At everypoint, a comparison is made between the image data (video level) and thescreen value (screen level) to determine if a pixel is to be turned OFFor ON. Thus, on its face, screening would appear to be a poor candidatefor fuzzy classification control because of its inability to be appliedat differing levels.

The other problem arises with determining filter coefficients. Imageprocessing requires that the filter coefficients sum to 1 (i.e., aconstant grey level in produces the same grey level out). If there isthe choice between several filters, their combination may no longer meetthis requirement.

The present invention resolves the first situation, the problem withapplying a screen in a fuzzy classification environment, by replacingthe screening operation with a linear operation that provides the sameeffect. Instead of comparing all video to a pre-defined screen, a 2-Dsinusoidal type function, with a maximum amplitude at 45 degrees, isadded to the incoming video. A more detailed explanation of this type ofscreening is disclosed in a co-pending U.S. patent application,application Ser. No. 08/285,328. The entire contents of this co-pendingU.S. patent application, application Ser. No. 08/285,328, are herebyincorporated by reference.

This screen, referred to as hybrid screening, creates video that is morelikely to cluster output pixels in an error diffusion process. Thisclustering of video is the effect that is very pleasing to the human eyein constant or near constant grey areas. It is the clustering effectthat makes this screened output superior to a straight non-screenederror diffused output.

The frequency characteristics of the 2-D hybrid screen determines theapproximate line pair per inch (lpi) dot pattern seen in the output;however, the ability to collect pixels into large dots, the desiredeffect in a screening process, is dependent on the screen's amplitude.This amplitude, a scalar, can be modified easily using the fuzzy imageclassification process of the present invention. All of the rules canassign a level (i.e., amplitude) of screening (large, medium, small ornone, etc.). The size and frequency of the screen are predetermined tomatch the characteristics of the printer.

The image processing of an image using a fuzzy classification process isvery similar to the fuzzy classification process itself. For example, inthe screening case, the fuzzy image processing method would establishthree screening classes (large, medium, and none) to determine thescreen value to be applied to pixel. Each of these classes would have aset of rules as in the fuzzy classification method to determine themembership value for the pixel in the screening processing class. Themembership value would then be used to calculate the actual screenamplitude which will be described in more detail below.

In a preferred embodiment of the present invention, the rules for thescreening classes are as follows:

If (pixel is "edge" or pixel is "low frequency halftone" or pixel is"mid frequency halftone" or pixel is "other"), then screen is NO₋₋SCREEN;

If (pixel is "black" or pixel is "white"), then screen is MEDIUM₋₋SCREEN; or

If (pixel is "pictorial" or pixel is "high frequency halftone"), thenscreen is FULL₋₋ SCREEN.

Referring to the multiple "If's" in the above rules, the membershipvalue of image in the class "NO₋₋ SCREEN" would be equal to the fuzzyvalue of the predicate, μ_(NO).sbsb.--SCREEN (screen)=max(μ_(edge)(pixel), μ_(lowfreqhalftone) (pixel), μ_(midfreqhaftone) (pixel),μ_(other) (pixel)); the membership value of image in the class "MEDIUM₋₋SCREEN" would be equal to the fuzzy value of the predicate,μ_(MEDIUM).sbsb.--SCREEN (screen)=max(μ_(black) (pixel), μ_(white)(pixel)); and the membership value of image in the class "FULL₋₋ SCREEN"would be equal to the fuzzy value of the predicate,μ_(FULL).sbsb.--SCREEN (screen)=max(μ_(pictoria) (pixel),μ_(highfreqhalftone) (pixel)).

To implement the fuzzy screening, the processing system determines themembership value of the pixel in each of the classes associated with aparticular screening process and assigns the maximum value as themembership value in the screening class. For example, if the pixel hadmembership values for "edge," "low frequency halftone," "mid frequencyhalftone," and "other" of 0.6, 0.7, 0.2, and 0.3, respectively, theprocessing system would decode the membership vector and assign amembership value to the NO₋₋ SCREEN class of 0.7. Moreover, if the pixelhad membership values for "black" and "white" of 0.6 and 0.5,respectively, the processing system would decode the membership vectorand assign a membership value to the MEDIUM₋₋ SCREEN class of 0.6.Lastly, if the pixel had membership values for "pictorial" and "highfrequency halftone" of 0.3 and 0.4, respectively, the processing systemwould decode the membership vector and assign a membership value to theFULL₋₋ SCREEN class of 0.4.

To determine the actual amplitude for the screen value, the fuzzyprocessing module, in the preferred embodiment of the present invention,multiply the membership value for each screen class with the idealcoefficient for that class and divide the product by the sum of themembership values. In the preferred embodiment, the NO₋₋ SCREENcoefficient is 0, MEDIUM₋₋ SCREEN coefficient is 0.5, and FULL₋₋ SCREENcoefficient is 1.0. Thus, in the example described above, the screenamplitude for the pixel being processed would be equal to a scalar valueof 0.412 (((1.0*0.4)+(0.5*0.6)+(0.0*0.7))/(0.4+0.6+0.7)).

An example of the application of fuzzy classification to screening isillustrated in FIG. 19. As illustrated in FIG. 19, the video signal orimage data is fed into a fuzzy classifier 80 which classifies the imagedata according to the rules described above. The fuzzy image classifier80 then generates a membership vector which is passed onto a screeninggenerating circuit 88. The screen generating circuit 88 produces ascreen value which is added to the image data at adder 89. The imagedata which is summed with the screen value corresponds to the same pixelas was classified by the fuzzy image classifier 80. In other words, thesystem illustrated in FIG. 19 also includes buffers (not shown) toinsure that the pixel being processed corresponds to the correctmembership vector being used to define the processing parameters.

A more detailed example of the application of fuzzy segmentation toscreening is illustrated in FIG. 20. As illustrated in FIG. 20, thevideo signal or image data is fed into a fuzzy classifier 80 whichclassifies the image data according to the rules described above. Thefuzzy image classifier 80 then generates a membership vector which ispassed onto a screening generating circuit 88. The screen generatingcircuit 88 includes a screening weighting circuit 883, a screen valuegenerating circuit 881, and a multiplier 885.

The screen weighting circuit 883 generates a weighting factor inresponse to the values in the membership vector so as to produce a noscreen, a medium screen, or a high screen, or any screen therebetween,as discussed above. In other words, if the pixel had membership valuesfor "edge," "low frequency halftone," "mid frequency halftone," and"other" of 0.8, 0.7, 0.1, and 0.3, respectively, the processing systemwould decode the membership vector and assign a membership value to theNO₋₋ SCREEN class of 0.8. Moreover, if the pixel had membership valuesfor "black" and "white" of 0.4 and 0.1, respectively, the processingsystem would decode the membership vector and assign a membership valueto the MEDIUM₋₋ SCREEN class of 0.4. Lastly, if the pixel had membershipvalues for "pictorial" and "high frequency halftone" of 0.2 and 0.0,respectively, the processing system would decode the membership vectorand assign a membership value to the FULL₋₋ SCREEN class of 0.2.

Thus, in this example, the screen amplitude for the pixel beingprocessed would be equal to a scalar value of 0.286(((1.0*0.2)+(0.5*0.4)+(0.0*0.8))/(0.2+0.4+0.8)).

The screen value generating circuit 881, which may be a lookup table ora hardwired circuit, produces a screen value based on the position ofthe pixel (image data) within the image. The weighting factor from thescreen weighting circuit 883 and the screen value from screen valuegenerating circuit 881 are multiplied together by multiplier 885 toproduce the screen value to be added to the pixel. This screen value isadded to the image data at adder 89.

The image data which is summed with the screen value corresponds to thesame pixel as was classified by the fuzzy image classifier 80. In otherwords, the system illustrated in FIG. 20 also includes buffers (notshown) to insure that the pixel being processed corresponds to thecorrect membership vector being used to define the processingparameters.

As noted above, the problem of using a fuzzy image classification systemto control digital filtering is two-fold. First, digital filtering isnot a scalar function, but a matrix function. Secondly, there is aconstraint on the matrix function; the filter must have gain of 1.0 at(ω₁ ω₂)=(0,0). This ensures that constant grey areas are not altered byfiltering.

To solve this problem, the present invention, in the preferredembodiment, uses the weighted summation of several pre-defined filtersto produced the filtered results. These filters are filters associatedwith a particular filter class; i.e., one filter class is enhance, onefilter class is lowpass, and another filter class is "other". Thedigital filter of the present invention takes the form of F_(o) =Σμ_(i)F_(i) where all of the F_(i) S correspond to the filters associated witheach i^(th) class (or classes) and μ_(i) corresponds to the membershipof the image in the ith class, as determined by the fuzzy processingroutine.

In a preferred embodiment of the present invention, the rules for thefiltering classes are as follows:

If (pixel is "edge" or pixel is "pictorial" or pixel is "low frequencyhalftone"), then filter is ENHANCE; or

If (pixel is "high frequency halftone"), then filter is LOWPASS.

Referring to the multiple "If's" in the above rules, the membershipvalue of image in the class "ENHANCE" would be equal to the fuzzy valueof the predicate, μ_(ENHANCE) (filter)=max(μ_(edge) (pixel),μ_(lowfreqhalftone) (pixel), μ_(pictorial) (pixel)); and the membershipvalue of image in the class "LOWPASS" would be equal to the fuzzy valueof the predicate, μ_(LOWPASS) (filter)=max (μ_(highfreqhalftone)(pixel)).

To implement the fuzzy screening, the processing system determines themembership value of the pixel in each of the classes associated with aparticular screening process and assigns the maximum value as themembership value in the screening class. For example, if the pixel hadmembership values for "edge," "low frequency halftone," and "pictorial"of 0.6, 0.7, and 0.3, respectively, the processing system would decodethe membership vector and assign a membership value to the ENHANCE classof 0.7. Moreover, if the pixel had membership values for "high frequencyhalftone" of 0.6, the processing system would decode the membershipvector and assign a membership value to the LOWPASS class of 0.6.

To determine the actual coefficients for the various filters, the fuzzyprocessing system must ensure that the fuzzy filtering, resulting fromthe rule set, meets the constraint of a gain of 1.0 at the frequency((ω₁ ω₂)=(0,0). To alleviate this problem, one of the output filterchoices is assigned the bypass function. In bypass, Vout=Vin; i.e.; nofiltering is done. Thus, the resulting filter, according to the conceptsof the present invention is F_(o) =Σμ_(i) F_(i) +(1-Σμ_(i)) * F_(B) suchthat when the desired effect is the bypass filter, the value goes tozero and the effect of the filter F_(i) is ignored.

It is noted that the enhancement filter amplifies all higherfrequencies, and the lowpass filter attenuates the higher frequencies.The coefficient value, c, for the bypass filter can be determined usingc_(bypass) =1-μ_(enhance) (filter)-μ_(lowpass) (filter) so that theoutput filter can be described as F_(o) =μ_(enhance) (filter) *F_(enhance) +μ_(lowpass) (filter) * F_(lowpass) +c_(bypass) *F_(bypass).

An example of image processing an image using fuzzy classification withrespect to filtering the image data is illustrated in FIG. 18. Asillustrated in FIG. 18, video data or image data is fed into a fuzzyclassifier 80, a lowpass filter 81, and an enhanced filter 82, and abypass filter 83 in parallel. As described above, the fuzzy classifier80 determines the membership vector of the pixel to be processed by theparallel set of filters. Note that this process includes buffers (notshown) to insure that the pixel being filtered corresponds to thecorrect membership vector being used to define the processingparameters.

Based upon this classification, the fuzzy classifier 80 will cause theoverall filter to generate a set of coefficients which are applied tomultipliers 84, 85, and 86. The coefficients will enable the overallfilter to weight the output from the various filters according to thefuzzy image classification.

For example, as noted above, if the pixel had membership values for"edge," "low frequency halftone," and "pictorial" of 0.6, 0.7, and 0.3,respectively, the processing system would decode the membership vectorand assign a membership value to the ENHANCE class of 0.7, which in turnis the filter coefficient assigned to the enhance filter 82 and fed tomultiplier 85. Moreover, if the pixel had membership values for "highfrequency halftone" of 0.6, the processing system would decode themembership vector and assign a membership value to the LOWPASS class of0.6, which in turn is the filter coefficient assigned to the lowpassfilter 81 and fed to multiplier 84.

This leaves the generation of the coefficient for the bypass filter 83.

As noted above, the generated coefficients need a relationship such thatthe sum of the coefficients will not be greater than 1 so as to keep thegain of the overall output from the filters to be equal to 1. Thus, inthe preferred embodiment of the present invention, the coefficient forthe bypass filter is 1 minus the enhance coefficient minus the lowpasscoefficient (in the example, 1-0.7-0.6=-0.3). This coefficient isapplied to the multiplier 86. The weighted filter outputs are then fedinto an adder 87 which adds all the outputs to produce a filtered pixelof image data which has been filtered according to its fuzzy imageclassification.

Although FIG. 18 illustrates the utilization of a fuzzy classifier witha plurality of different filters, the fuzzy classifier can also beutilized in connection with a modular time variant two-dimensionalnon-separable filter wherein the non-separable filter is made up of aplurality of separable filters.

The utilization of separable filters allows a non-separable filter of aparticular form to be implemented in a cost effective manner. Moreover,a separable two-dimensional filter can be described as one-dimensionalfilter that acts in the vertical direction or slowscan directionfollowed by another one-dimensional filter acting in the horizontal orfastscan direction. Thus, the filter matrix can be described as theproduct of F_(vh) =f_(v) *f_(h) wherein F_(vh) is an N by M matrix,f_(v) is a N length column vector (the vertical filter) and f_(h) is a Mlength row vector (the horizontal filter).

If the matrix F_(vh) cannot be represented using the above equation, thematrix is considered non-separable and the separable filterimplementation cannot be used. However, if the N by M matrix isrepresented using a singular value decomposition such as F_(vh) =U*S*V,where U is N by N unitary matrix, S is an N by M diagonal matrix, and Vis a M by M unitary matrix; a separable filter implementation can beused. Furthermore, if N and M are not equal, the above equation can bealtered to F_(vh) =U_(r) *S_(r) *V_(r), where Q=min(n,m), U_(r) is a Nby X submatrix of U, S_(r) is a Q by Q submatrix of S, and V_(r) is a Mby Q submatrix of V. Putting this in summation form, the resultingrepresentation will be F_(vh) =Σs(i)*u_(i) *v_(i), where i is greaterthan or equal to 1 but less than or equal to Q.

In this representation, the vector s(i) is a diagonal of the matrixS_(r), u_(i) is the i^(th) column vector of U_(r), and v_(i) is thei^(th) column vector of V_(r). Each component is a separable filtersimilar to that described above with the exception of a gain factors(i). In other words, any non-separable filter of length N by M can berepresented as the weighted summation of Q separable N by M filters.Thus, to implement a non-separable filter using the weighted averages ofseveral of the filters, the hardware becomes a conglomeration of Qseparable filter modules, Q multipliers, and an adder. Such a hardwareimplementation, according to the concepts of the present invention isillustrated in FIG. 3.

The non-separable filter 1 of FIG. 3 includes a plurality of separablefilters (2, 4, 6, and 8). Each of these separable filters include avertical filter and a horizontal filter thus making the separable filtera separable two-dimensional filter. For example, the two-dimensionalseparable filter 2 includes a vertical filter 3 and a horizontal filter5.

It is noted that if the non-separable filter 1 is a 3-dimensionalfilter, the individual separable filters 2, 4, 6, and 8 would eachinclude 3 individual filters each filtering a separate dimension of thespace. Such three dimensional filters are valuable when filtering movingimage data such as in a motion picture or video recording.

The output of each separable filter is fed into a multiplier whichmultiplies the output of the separable two dimensional filter by a gainfactor s(i). For example, the output from the two-dimensional separablefilter 2 is fed into multiplier 10 and multiplied by the gain factor s1.

The gain factor can be a predetermined gain factor or a gain factor setby the fuzzy classifier described above. In the preferred embodiment ofthe present invention, the gain factor s(i) is set by the fuzzyclassifier in accordance with the identified image type or class of thepixel being processed as described above.

The outputs from each of the multipliers are fed into an adder 18 whichsums all the outputs to produce a two-dimensionally filtered pixel ofimage data. It is noted, that the summation of all of the gain factorss(i) is equal to 1 to ensure that the overall gain of the non-separablefilter 1 is equal to 1. It is further noted that each separable filterreceives the same pixel of image data in parallel. Moreover, eachseparable two-dimensional filter is supplied with the coefficientsneeded to perform the two-dimensional filtering on that particularpixel. These coefficients may be predetermined or they may be selectedfrom a set of different coefficients depending on the image type orclass of the pixel of image data being processed.

FIG. 4 illustrates the relationship of a non-separable two-dimensionalfilter in an image processing scheme. As illustrated in FIG. 4, videodata is fed in parallel to a segmentation circuit 300 and buffers 306.The segmentation circuit 300 may either be a conventional imagesegmentation circuit or the fuzzy segmentation circuit discussed above.The segmentation circuit 300 determines the image type or class of thepixel of image data being processed. This image type or classinformation is fed into a weighting coefficients and gain factorgenerator 302 which generates the weighting coefficients and gainfactors to be utilized by the non-separable two dimensional filter 304.

The segmentation circuit 300 also generates other information withrespect to the image type or class which are called effects to beutilized further downstream in the image processing system. Thenon-separable two-dimensional filter 304 utilizes the weightingcoefficients and gain factors to properly filter the pixels of imagedata received from buffer 306 to produce a two-dimensionally filteredpixel of image data.

When compared to a direct implementation of a separable N by M filter,the implementation of the non-separable filter utilizing a plurality ofseparable filters makes the design of an N by M filter very modular.This modularity makes the redesign of a new filter much faster as onlythe individual separable filter components need to be redone. Inaddition, if the separable module can be easily configured for manydifferent sizes, the non-separable module inherits the sameconfigurability.

Another major advantage of this type of implementation for anon-separable filter is that because of its modular nature, a filter canbe implemented using the minimum amount of hardware. For example,assuming that an 11×15 non-separable filter is desired, after performinga singular value decomposition on the 11×15 non-separable filter, onerealizes that only two elements of the gain vector s(i) are non zero andthus the filter matrix for the 11×15 filter has a rank of 2. This alsowould be true if the gain vector s(i) had only 2 elements that were muchlarger than all the other elements of gain vector s(i). In such a case,the filter can be implemented in hardware using only 2 separable filterblocks, not 11 blocks, as would be required if the filter matrix were offull rank.

In addition to being useful for implementing non-separable filters, theimplementation of the present invention can be easily adapted toimplement time-varying filters. The separable filters may not have theability to time vary their coefficients hence the separable filterscannot be used to implement a time varying filter. However, using theconcepts of the present invention, these filters can be used with someminimal additional hardware to create a time-varying non-separablefilter.

For example, the non-separable filter described above can be madetime-varying by making the vector s(i) a function of time and position.If the vector s(i) is not static, but instead is time-varying, theoverall filter can be time-varying as well. If desired, the vectorvalues can change on sample by sample or on a pixel by pixel basis. Thetime-varying effect can be attained by having only a few programmablegains; i.e., not all the coefficients need to be time-varying to havegood time-varying characteristic. Although there is not total freedom inchoosing the filter's performance given a certain time and location, theimplementation of the present invention provides several degrees offreedom that greatly enhance the functionality of the filters sufficientfor many practical applications.

For example, suppose that there are three different separable filters,one is a lowpass filter, one is a mid range (bandpass) filter, and oneis a high pass filter. Furthermore, it is desirable to implement acombination of these filters that varies over time. Additionally, atsome given instance, the lowpass filter is the desired effect. In such ascenario, the weighting on the lowpass filter would be 1 with zeroweighting on the other two filters; i.e., the s(i) vector would equal 10 0!. At another point in time, it may be necessary to block the lowpassfilter and attenuate the mid range filter by a factor of 0.5 and amplifythe high pass filter by 1.2. This is accomplished by assigning thevalues of the s(i) vector to be equal to 0 0.5 1.2!. It is noted that abypass filter would be included in these examples to ensure a gain of1.0 as described above with respect to fuzzy processing.

An application where this flexibility provides a needed functionality isin providing filtering as required when using fuzzy image segmentationor fuzzy image processing. In the fuzzy situation, there are differentpre-defined classes, each associated with a desired filter. Moreoverwhen utilizing fuzzy image segmentation, a pixel is not assigned oneclass but is assigned a membership at each of the pre-defined classes.Dependent on the segmentation class vector, the desired filter will be aweighted summation of each of the associated filters.

More specifically, for example, in a situation utilizing a two classfuzzy classifier, the first class may require the object to be lowpassfiltered and the second class requires the object to be high passfiltered. If the object is of the type of the first class, the lowpassfilter is implemented; however, if the object of the second class, thehigh pass filter is implemented.

After fuzzy image segmentation, the object is determined to have amembership of 0.7 in the first class and 0.3 in the second class. Such adetermination will result, using the concepts of the present invention,in the decision to implement a filter that is the summation of 0.7 ofthe lowpass filter and 0.3 of the high pass filter. Such animplementation can be easily accomplished utilizing the filterillustrated in FIG. 22 by setting the s(i) vector to be equal to 0.7 0.30!. If the next pixel is classified differently, its associated filterwill be formed by changing the vector s(i) to produce the desiredcoherent filter.

Typically, a two-dimensional filter is implemented in hardware as afixed size filter such as an N by M filter. Many times, the filter isnot self-timed, which means that its output will be larger than theoriginal input since convolution changes the size of a fixed lengthinput. This not a desirable characteristic for processing documentswhere there is a size constraint due to the size of the display media;i.e., screen size or paper size. To address this situation, a self-timedtwo-dimensional filter is proposed.

A self-timed filter is one that does not require the external system tofeed it additional information such as scanlines, pixels, or controlsignals before or after the input line or page has been processed topreserve the dimensions of the output page. Consequently, the number ofscanlines and pixels per scanline in the output page is kept equal tothe number of scanlines and pixels per scanline as the input page. Theself-timed filter is not limited to a particular size, but can beprogrammed to any size less than a maximum N by M wherein N is thenumber of elements in the slowscan direction and M is the number ofelements in the fastscan direction.

The self-timed two-dimensional filter of the present invention isaccomplished using the separable implementation described above withrespect to the non-separable two-dimensional filter. In this separableimplementation, the input video undergoes filtering in the slowscandirection (vertical direction) followed by filtering in the fastscandirection (horizontal direction). Although the slowscan and fastscanfilters may reside on the same chip, each filter acts independently ofthe other.

In a Q by R implementation (Q is less than or equal to N and R is lessthan or equal to M), the filter is connected to Q-1 external FIFO thatare used to buffer video for the slowscan direction. In addition, windowpointers, (tags that are passed along with the input video and describespixel characteristics) which must correspond to the center slowscanelement, are buffered by (Q-1)/2 FIFOs. The final video output of thefilter can be filtered video or unfiltered video wherein the unfilteredvideo is produced during the bypass mode. The bypass path mode is chosenwhen there is not enough context to enable proper filtering such asaround the perimeter of page.

During the processing of the input page, the filter will not produce anyoutput while the first (Q-1)/2 scanlines are being input to the filter.However, on the scanline following the ((Q-1)/2)^(th) scanline, theself-timed filter will begin to output video in the bypass mode. Due toa lack of context in the bypass mode, the center slowscan element, whichis created by buffering the input video with (Q-1)/2 FIFOs, will exitthe filter unchanged during the bypass mode. A Q by R filter requires Qlines of context before it can begin filtering. Consequently, the filterwill remain in the bypass mode for (Q-1)/2 scanlines until it begins theQth scanline at which time it will begin filtering and will outputfiltered video. It will stop filtering video when the last scanline ofthe input page has been processed.

Since the filter did not begin to output data until the scanlinefollowing the ((Q-1)/2)^(th) scanline, (Q-1)2 additional scanlines haveto be created at the end of an input page to preserve the total numberof scanlines in the output page. To address this situation, theself-timed two-dimensional N by M filter creates the (Q-1)/2 additionalscanlines once the input page has ended to preserve the total number ofscanlines in the output page.

In this implementation, a scan page is described by three signals;pagesync, linesync, and video valid. The first signal, pagesync,determines when a page is active. The second signal, linesync,determines when a scanline is active. The third signal is a video orpixel enable signal that indicates valid video during a scanline. Thecreation of (Q-1)/2 additional scanlines once the input page has endedis done by creating a new set of internal signals that function as thepage active, line active, and video enable signals for the rest of thefilter. These signals also control the flushing of the last ((Q-1)/2)scanlines of video that are stored in the FIFO.

FIG. 5 illustrates an example of a self-timed 9×15 two-dimensionalfilter utilizing the concepts of the present invention. As illustratedin FIG. 5, the self-timed 9×15 filter 500 receives the input signalslinesync, video valid, pagesync, and system clock. These signals arebuffered and then fed into a control logic circuit 502 which generatesthe internal signal used for the self-timing mechanism. In the preferredembodiment of the present invention, the new internal signals are newlinesync, new video valid, and new pagesync.

These three new signals are fed into the non-separable two-dimensionalfiltering circuit 501 which implements the concepts illustrated in FIG.3.

The self-timed non-separable two-dimensional filter 500 also receives 9scanlines of video data wherein 8 scanlines of video data are fed intoFIFOs prior to entering the non-separable two-dimensional filteringcircuit 501.

In this situation, while the first 4 scanlines of the input page arebeing input into the self-timed two-dimensional filter 500, theself-timed two-dimensional filter 500 will not output any video data.When the self-timed two-dimensional filter 500 begins to process thefifth scanline of the input page, the filter 500 will begin to outputvideo data in the bypass mode and continue to process video in this modeuntil the ninth scanline of the input page is received. At this time,the self-timed two-dimensional filter 500 will actually begin thefiltering process.

The self-timed two-dimensional filter 500 will continue to filter videodata until the last line of the active page has ended. At this time, theself-timed two-dimensional filter 500 will generate four additionallinesync signals through internal signal new linesync, thereby flushingout the last four scanlines of video data from the FIFO buffers andoutputting this video in the bypass mode.

To facilitate a better understanding of the concepts of the self-timedtwo-dimensional filter as illustrated in FIG. 5, a brief explanation ofthe timing aspect of this filter will be discussed below in reference tothe timing diagram illustrated in FIG. 6. More specifically, FIG. 6illustrates the input signals pagesync and linesync (A and B), theinternal signals new pagesync and new linesync (C and D), and the outputsignals output pagesync and output linesync (E and F). Moreover, FIG. 6illustrates the timing diagram of a self-timed two-dimensional filterhaving dimensions of 9 by 15.

As illustrated in FIG. 6, when the pagesync signal becomes active, theself-timed two-dimensional filter causes the internal new pagesyncsignal to also become active. Moreover, when the self-timedtwo-dimensional filter receives an active linesync signal, theself-timed two-dimensional filter causes the internal new linesyncsignal to also become active. However, in this example, the outputpagesync signal and the output linesync signal are not caused to beactive until a later time.

More specifically, the output pagesync signal does not become activeuntil the fifth input linesync signal is received. At this point, theoutput pagesync signal becomes active and the first output linesyncsignal becomes active. This point in time also corresponds to thebeginning of the first of the bypass mode wherein the self-timedtwo-dimensional filter outputs unfiltered video data since the filterhas not received enough scanlines of video data to properly beginfiltering. This bypass mode continues until the ninth input linesyncsignal is received wherein the self-timed two-dimensional filterswitches to the filtering mode and outputs properly filtered image data.

At some later time, when the received pagesync signal becomes inactiveindicating that the input page has ended, the self-timed two-dimensionalfilter causes the internal new pagesync signal to remain active for anadditional four scanline cycles so as to flush out the buffers. Uponreceiving an inactive pagesync signal, the self-timed two-dimensionalfilter switches from the filtering mode back to the bypass mode so as toflush out the FIFOS with unfiltered video data.

Although the two-dimensional filters of the present invention has beendescribed with respect to a single filter architecture, thetwo-dimensional filters of the present invention can also be implementedas a plurality of cascadable filters. In other words, a largetwo-dimensional filter can be built by using many smaller cascadablefilters. These cascadable filters are separable and symmetric and may beof any size N by M wherein N is the number of slowscan elements and M isthe number of fastscan elements.

The cascadable filter has an extra cascade input and cascade output thatare only used when multiple filters are cascaded together. Thus, anynumber of cascadable filters can be cascaded together to provide thefilter size. For example, when two (N×M) filters are cascaded together,the maximum resulting filter can be an 2N-1×2M-1. Moreover, when three(N×M) filters are cascaded together, the maximum resulting filter can be3N-1×3M-1, etc. Also, when two N filters are cascaded together andcombined with a M filter, a (2N-1×M) filter is created.

To explain the cascadable concept more clearly, the implementation of a7×15 filter will be utilized as an example. More over, in this example,the individual components of the two-dimensional filter (the slowscanfilter and the fastscan filter) will be examined individually.

As noted above, FIG. 7 illustrates a cascadable 7 element slowscanfilter. This cascadable slowscan filter receives pixels of image datafrom 7 different scanlines wherein a pair of pixels of image data arefed to each adder 701, 702, 703, and 704. It is noted that since FIG. 7illustrates a 7 element slowscan cascadable filter, the input X to adder704 is tied to zero.

The output from each adder is fed into a separate multiplier 705, 706,707, and 708 wherein the individual sums are multiplied by the weightingcoefficients A, B, C, D, respectively. The weighted results from themultipliers are then fed into adder 709 which produces a 7 elementslowscan filtered pixel of image data. The output from adder 709 is fedto adder 710 which sums the 7 element slowscan filtered pixel of imagedata with the filtered image data from another cascadable slowscanfilter. This input can be tied to zero if it is not desirable to make afilter greater than 7 elements.

Suppose that it is desirable to cascade 2 cascadable slowscan filters tocreate one that is 15 elements wide. More specifically, it is desirableto cascade 2 slowscan filters which will generate image data that isrepresented by a value that is equal to A(line 0+line 14)+B(line 1+line13)+C(line 2+line 12)+D(line 3+line 11)+E(line 4+line 10)+F(line 5+line9)+G(line 6+line 8)+H(line 7).

Conventionally, such a 15 element slowscan filter would be implementedas illustrated in FIG. 8. In other words, each of the 8 operationsdescribed above to generate the proper value for the pixel of image datawould be implemented in parallel and fed to a single adder 16. However,as noted above, such a large filter can be implemented utilizing aplurality of cascadable filters.

More specifically, as illustrated in FIG. 9, the 15 element slowscanfilter can be implemented utilizing two smaller cascadable filters 800and 900. In this example, each cascadable filter is similar to thecascadable filter as discussed above with respect to FIG. 7. However inthis case, the cascadable filter 900 receives the cascadable outfiltered image data from cascadable filter 800 and inputs this cascadedfiltered image data into adder 910. Adder 910 adds together the cascadedfiltered image data from cascadable filter 800 with the filtered imagedata produced by the various elements of cascadable filter 900 toproduce a final filtered result which represents a pixel of image datawhich has been filtered by a 15 element slowscan filter.

In other words, the output from adder 910 generates a value which isrepresented by the equation given above. Moreover, the inclusion of anadditional cascadable filter, as illustrated in FIG. 7, can result in a23 element slowscan filter. Moreover, the inclusion of 2 more cascadablefilters, as illustrated in FIG. 7, can result in a 31 element slowscanfilter.

Not only can the cascadable filter concept be applied to the slowscandirection, it is also applicable to the fastscan direction. FIG. 10illustrates a fastscan cascadable filter wherein input 321 representsthe cascadable output from another fastscan cascadable filter. If onedesires that the present cascadable filter, as illustrated in FIG. 10,to remain a 15 element fastscan filter, the input 321 is tied to zeroand the input to adder 315 is also tied to zero.

If it is desired to implement a 31 element wide fastscan filter, onewould cascade together two fastscan filters as illustrated in FIG. 10wherein the output 320 of the first cascadable filter would be input tothe input terminal that is equivalent to the input terminal 321 in thesecond cascadable fastscan filter. In this way, two smaller fastscanfilters can be cascaded to create a larger fastscan filter.

An example of a chip having a cascadable filtering architecture isillustrated in FIG. 25. The filtering chip 2000 includes N cascadablefilters (2001, 2005, 2011, 2015, and 2017). The filtering chip 2000 alsoincludes N multiplexers (2003, 2007, 2013, and 2019) wherein a singlemultiplexer is connected between two cascadable filters. Themultiplexers receive the cascadable filtered image data from thedownstream filter (i.e., multiplexer 2003 receives the cascadablefiltered image data from filter1 2001) and a zero value at themultiplexers two input terminals.

If the output of the downstream filter is to cascaded into the nextfilter, a logically enabling signal is received by the multiplexer sothat the filtered data is input into the upstream filter. For example,if filter1 2001 and filter2 2005 where to be cascaded together,multiplexer 2003 would receive a logically enabling signal, causingmultiplexer 2003 to pass the filtered output of filter1 2001 to filter22005. If the two filters were not cascaded together, the multiplexer2003 would receive a logically non-enabling signal, causing multiplexer2003 to pass on a zero value to filter2 2005, thereby preventing thecascading of the two filters.

Although the present invention has been described in detail above,various modifications can be implemented without departing from thespirit of the present invention. For example, the preferred embodimentof the present invention has been described with respect to axerographic printing system; however, these fuzzy methods and filtersare readily implemented in a thermal inkjet system, a display system, orother image processing system.

Moreover, the image processing system of the present invention can bereadily implemented on a general purpose computer, a personal computeror workstation. The image processing system of the present invention canbe readily implemented on an ASIC, thereby enabling the placement ofthis process in a scanner, electronic subsystem, printer, or displaydevice.

The present invention has been described with respect to a video rangeof 0 to 255. However, it is contemplated by the present invention thatthe video range can be any suitable range to describe the grey level ofthe pixel being processed. Furthermore, the present invention is readilyapplicable to any image processing system, not necessarily a binaryoutput device. It is contemplated that the concepts of the presentinvention are readily applicable to a four-level output terminal orhigher.

Also, the present invention has been described, with respect to thefuzzy classification and fuzzy processing routines, that the scalarvalues are determined using the weighted sum of the centriod methodsince the centriods in the preferred embodiment are non-overlapping (theclasses are non-overlapping). However, the present invention is readilyapplicable to a system with overlapping classes. Such an extension isreadily known to those skilled in the art of fuzzy logic.

Lastly, the present invention has been described with respect to amonochrome or black/white environment. However, the concepts of thepresent invention are readily applicable to a color environment. Namely,the image processing operations of the present invention can be appliedto each color space value representing the color pixel.

While the invention has been described with reference to variousembodiments disclosed above, it is not confined to the details set forthabove, but is intended to cover such modifications or changes as maycome within the scope of the attached claims.

What is claimed is:
 1. A method for electronically image processing apixel belonging to a set of digital image data with respect to amembership of the pixel in a plurality of image classes and an otherclass, comprising the steps of:(a) receiving a set of digital image dataincluding the pixel; (b) determining a membership value for the pixelfor each image class and a membership value for the pixel for the otherclass, each image class being defined by a set of heuristic rules, thesets being non-mutually exclusive, the membership value for an imageclass being independent of the other membership values for the imageclasses, the membership value for the other class being dependent on themembership values of the image classes; (c) generating a classificationvector for the pixel based on the determination in said step (b); and(d) image processing the pixel based on membership values in theclassification vector of the pixel.
 2. The method as claimed in claim 1,wherein said step (d) includes the substep of two-dimensionallyfiltering the pixel of image data.
 3. The method as claimed in claim 2,wherein the two-dimensionally filtering substep includes the substepsof:(d1) applying a plurailty of filters in parallel to the pixel ofimage data; (d2) weighting the output from each filter based on a set ofweighting coefficients; and (d3) adding the weighted outputs to producea filtered pixel of image data.
 4. The method as claimed in claim 3,wherein each parallel filter performs the substeps of:(d4) applying aplurality of first dimensional filters to the pixel of image data, thefiltering being based on a set of first dimensional filter coefficients;and (d5) applying a plurality of second dimensional filters to the pixelof image data filtered by said substep (d4), the filtering being basedon a set of second dimensional filter coefficients.
 5. The method asclaimed in claim 1, wherein said step (d) includes the substep ofscreening the pixel of image data.
 6. The method as claimed in claim 1,wherein the set of non-mutually exclusive image classes include text andcontone.
 7. The method as claimed in claim 1, wherein the set ofnon-mutually exclusive image classes include text, contone, andhalftone.
 8. The method as claimed in claim 1, wherein the set ofnon-mutually exclusive image classes include edge, black, white,pictorial, low frequency halftone, mid frequency halftone, and highfrequency halftone.
 9. The method as claimed in claim 1, wherein saidstep (b) includes the substeps of:(b1) calculating a summation ofabsolute values of Laplacians of a window of pixels around the pixel tobe classified; (b2) determining a grey level range within the window ofpixels; (b3) determining a two-dimensional frequency around the pixel beclassified; (b4) determining a percentage of pixels in the window havinga grey value less than a predetermined black threshold; and (b5)determining a percentage of pixels in the window having a grey valuegreater than a predetermined white threshold.
 10. The method as claimedin claim 9, wherein said step (b) further includes the substeps of:(b6)determining a conditional value of the pixel with respect to a conditionof the summation of said substep (b1) being greater than a firstpredetermined value, determining a conditional value of the pixel withrespect to a condition of the determined range of said substep (b2)being greater than a second predetermined value, and determining aconditional value of the pixel with respect to a condition of thedetermined frequency of said substep (b3) being greater than a thirdpredetermined value; (b7) establishing a conditional value for the pixelwith respect to a halftone class by determining which conditional valuedetermined in said substep (b6) is a minimum value and assigning theminimum value as the conditional value; (b8) determining a conditionalvalue of the pixel with respect to a condition of the summation of saidsubstep (b1) being greater than a fourth predetermined value,determining a conditional value of the pixel with respect to a conditionof the determined range of said substep (b2) being greater than a fifthpredetermined value, and determining a conditional value of the pixelwith respect to a condition of the determined frequency of said substep(b3) being greater than the third predetermined value; (b9) establishinga membership value for the pixel with respect to an edge class bydetermining which conditional value determined in said substep (b8) is aminimum value and assigning the minimum value as the membership value;(b10) determining a conditional value of the pixel with respect to acondition of the summation of said substep (b1) being less than thefirst predetermined value and determining a conditional value of thepixel with respect to a condition of the determined frequency of saidsubstep (b3) being less than the third predetermined value; and (b11)establishing a conditional value for the pixel with respect to a contoneclass by determining which conditional value determined in said substep(b10) is a minimum value and assigning the minimum value as theconditional value.
 11. The method as claimed in claim 10, wherein saidstep (b) further includes the substeps of:(b12) determining aconditional value of the pixel with respect to a condition of thepercentage determined in said substep (b4) being greater than a sixthpredetermined value; (b13) establishing a membership value for the pixelwith respect to a black class by determining which conditional valuedetermined in said substeps (b11) and (b12) is a minimum value andassigning the minimum value as the membership value; (b14) determining aconditional value of the pixel with respect to a condition of thepercentage determined in said substep (b5) being greater than a seventhpredetermined value; (b15) establishing a membership value for the pixelwith respect to a white class by determining which conditional valuedetermined in said substeps (b11) and (b14) is a minimum value andassigning the minimum value as the membership value; (b16) determining aconditional value of the pixel with respect to a condition of thepercentage determined in said substep (b4) being less than the sixthpredetermined value and determining a conditional value of the pixelwith respect to a condition of the percentage determined in said substep(b5) being less than the seventh predetermined value; and (b17)establishing a membership value for the pixel with respect to apictorial class by determining which conditional value determined insaid substeps (b11) and (b16) is a minimum value and assigning theminimum value as the membership value.
 12. The method as claimed inclaim 11, wherein said step (b) further includes the substeps of:(b18)determining a conditional value of the pixel with respect to a conditionof the frequency determined in said substep (b3) being less than aneighth predetermined value; (b19) establishing a membership value forthe pixel with respect to a low frequency halftone class by determiningwhich conditional value determined in said substeps (b7) and (b18) is aminimum value and assigning the minimum value as the membership value;(b20) determining a conditional value of the pixel with respect to acondition of the frequency determined in said substep (b3) being greaterthan the eighth predetermined value and determining a conditional valueof the pixel with respect to a condition of the frequency determined insaid substep (b3) being less than a ninth predetermined value; (b21)establishing a membership value for the pixel with respect to a midfrequency halftone class by determining which conditional valuedetermined in said substeps (b7) and (b20) is a minimum value andassigning the minimum value as the membership value; (b22) determining aconditional value of the pixel with respect to a condition of thefrequency determined in said substep (b3) being greater than the ninthpredetermined value; and (b23) establishing a membership value for thepixel with respect to a high frequency halftone class by determiningwhich conditional value determined in said substeps (b7) and (b22) is aminimum value and assigning the minimum value as the membership value.13. The method as claimed in claim 12, wherein said step (b) furtherincludes the substep of:(b24) establishing a membership value for thepixel with respect to the other class by determining which membershipvalue determined in said substeps (b9), (b13), (b15), (b17), (b19),(b21), and (b23) is a maximum value and assigning a value equal to oneminus the maximum value as the membership value for the other.
 14. Asystem for electronically image processing a pixel belonging to a set ofdigital image data with respect to a membership of the pixel in aplurality of image classes and an other class, comprising:fuzzyclassifying means for determining a membership value for the pixel foreach image class and a membership value for the pixel for the otherclass, each image class being defined by a set of heuristic rules, saidsets being non-mutually exclusive, the membership value for an imageclass being independent of the other membership values for the imageclasses, the membership value for the other class being dependent on themembership values of the image classes; effect means for generating aclassification vector for the pixel based on the determination by saidfuzzy classifying means; and processing means for image processing thepixel based membership values in the classification vector of the pixel.15. The system as claimed in claim 14, wherein said processing meansincludes a two-dimensional filter.
 16. The system as claimed in claim15, wherein said two-dimensional filter includes:a plurality of filtersto filter the image data in parallel; a set of multipliers to weight theoutput from each filter based on a set of weighting coefficients; and anadder to add the weighted outputs to produce a filtered pixel of imagedata.
 17. The system as claimed in claim 16, wherein each parallelfilter includes:a plurality of first dimensional filters to filter thepixel of image data, the filtering being based on a set of firstdimensional filter coefficients; and a plurality of second dimensionalfilters to filter the pixel of image data filtered by said firstdimensional filters, the filtering being based on a set of seconddimensional filter coefficients.
 18. The system as claimed in claim 14,wherein said processing means includes a screening circuit to screen thepixel of image data.
 19. A method for electronically screening a pixelbelonging to a set of digital image data with respect to a membership ofthe pixel in a plurality of image classes and an other class, comprisingthe steps of:(a) determining a membership value for the pixel for eachimage class and a membership value for the pixel for the other class,each image class being defined by a set of heuristic rules, the setsbeing non-mutually exclusive, the membership value for an image classbeing independent of the other membership values for the image classes,the membership value for the other class being dependent on themembership values of the image classes; (b) generating a classificationvector for the pixel based on the determination in said step (a); and(c) screening the pixel based membership values in the classificationvector associated with the pixel.
 20. The method as claimed in claim 19,wherein said step (c) includes the substeps of:(c1) generating a screenvalue according to a position of the pixel in the set of digital imagedata; (c2) generating a screen amplitude weighting value based on themembership values in the classification vector for the pixel; (c3)multiplying the screen value and the screen amplitude weighting value toproduce a modified screen value; and (c4) adding the modified screenvalue to the pixel of image data.
 21. The method as claimed in claim 20,wherein said step (a) includes the substeps of:(a1) calculating asummation of absolute values of Laplacians of a window of pixels aroundthe pixel to be classified; (a2) determining a grey level range withinthe window of pixels; (a3) determining a two-dimensional frequencyaround the pixel be classified; (a4) determining a percentage of pixelsin the window having a grey value less than a predetermined blackthreshold; and (a5) determining a percentage of pixels in the windowhaving a grey is value greater than a predetermined white threshold. 22.The method as claimed in claim 21, wherein said step (a) furtherincludes the substeps of:(a6) determining a conditional value of thepixel with respect to a condition of the summation of said substep (a1)being greater than a first predetermined value, determining aconditional value of the pixel with respect to a condition of thedetermined range of said substep (a2) being greater than a secondpredetermined value, and determining a conditional value of the pixelwith respect to a condition of the determined frequency of said substep(a3) being greater than a third predetermined value; (a7) establishing aconditional value for the pixel with respect to a halftone class bydetermining which conditional value determined in said substep (a6) is aminimum value and assigning the minimum value as the conditional value;(a8) determining a conditional value of the pixel with respect to acondition of the summation of said substep (a1) being greater than afourth predetermined value, determining a conditional value of the pixelwith respect to a condition of the determined range of said substep (a2)being greater than a fifth predetermined value, and determining aconditional value of the pixel with respect to a condition of thedetermined frequency of said substep (a3) being greater than the thirdpredetermined value; (a9) establishing a membership value for the pixelwith respect to an edge class by determining which conditional valuedetermined in said substep (a8) is a minimum value and assigning theminimum value as the membership value; (a10) determining a conditionalvalue of the pixel with respect to a condition of the summation of saidsubstep (a1) being less than the first predetermined value anddetermining a conditional value of the pixel with respect to a conditionof the determined frequency of said substep (a3) being less than thethird predetermined value; and (a11) establishing a conditional valuefor the pixel with respect to a contone class by determining whichconditional value determined in said substep (a10) is a minimum valueand assigning the minimum value as the conditional value.
 23. The methodas claimed in claim 22, wherein said step (a) further includes thesubsteps of:(a12) determining a conditional value of the pixel withrespect to a condition of the percentage determined in said substep (a4)being greater than a sixth predetermined value; (a13) establishing amembership value for the pixel with respect to a black class bydetermining which conditional value determined in said substeps (a11)and (a12) is a minimum value and assigning the minimum value as themembership value; (a14) determining a conditional value of the pixelwith respect to a condition of the percentage determined in said substep(a5) being greater than a seventh predetermined value; (a15)establishing a membership value for the pixel with respect to a whiteclass by determining which conditional value determined in said substeps(a11) and (a14) is a minimum value and assigning the minimum value asthe membership value; (a16) determining a conditional value of the pixelwith respect to a condition of the percentage determined in said substep(a4) being less than the sixth predetermined value and determining aconditional value of the pixel with respect to a condition of thepercentage determined in said substep (a5) being less than the seventhpredetermined value; and (a17) establishing a membership value for thepixel with respect to a pictorial class by determining which conditionalvalue determined in said substeps (a11) and (a16) is a minimum value andassigning the minimum value as the membership value.
 24. The method asclaimed in claim 23, wherein said step (a) further includes the substepsof:(a18) determining a conditional value of the pixel with respect to acondition of the frequency determined in said substep (a3) being lessthan an eighth predetermined value; (a19) establishing a membershipvalue for the pixel with respect to a low frequency halftone class bydetermining which conditional value determined in said substeps (a7) and(a18) is a minimum value and assigning the minimum value as themembership value; (a20) determining a conditional value of the pixelwith respect to a condition of the frequency determined in said substep(a3) being greater than the eighth predetermined value and determining aconditional value of the pixel with respect to a condition of thefrequency determined in said substep (a3) being less than a ninthpredetermined value; (a21) establishing a membership value for the pixelwith respect to a mid frequency halftone class by determining whichconditional value determined in said substeps (a7) and (a20) is aminimum value and assigning the minimum value as the membership value;(a22) determining a conditional value of the pixel with respect to acondition of the frequency determined in said substep (a3) being greaterthan the ninth predetermined value; and (a23) establishing a membershipvalue for the pixel with respect to a high frequency halftone class bydetermining which conditional value determined in said substeps (a7) and(a22) is a minimum value and assigning the minimum value as themembership value.
 25. The method as claimed in claim 24, wherein saidstep (a) further includes the substep of:(a24) establishing a membershipvalue for the pixel with respect to the other class by determining whichmembership value determined in said substeps (a9), (a13), (a15), (a17),(a19), (a21), and (a23) is a maximum value and assigning a value equalto one minus the maximum value as the membership value for the other.26. A method for electronically filtering a pixel belonging to a set ofdigital image data with respect to a membership of the pixel in aplurality of image classes and an other class, comprising the stepsof:(a) determining a membership value for the pixel for each image classand a membership value for the pixel for the other class, each imageclass being defined by a set of heuristic rules, the sets beingnon-mutually exclusive, the membership value for an image class beingindependent of the other membership values for the image classes, themembership value for the other class being dependent on the membershipvalues of the image classes; (b) generating a classification vector forthe pixel based on the determination in said step (a); and (c)two-dimensionally filtering the pixel based on membership values in theclassification vector associated with the pixel.
 27. The method asclaimed in claim 26, wherein said step (c) includes the substeps of:(c1)lowpass filtering the pixel; (c2) highpass filtering the pixel; (c3)non-filtering the pixel; (c4) multiplying the lowpass filtered pixel bya gain factor based on the membership values in the classificationvector associated with the pixel; (c5) multiplying the highpass filteredpixel by a gain factor based on the membership values in theclassification vector associated with the pixel; (c6) multiplying thenon-filtered pixel by a gain factor based on the membership values inthe classification vector associated with the pixel; and (c7) adding theproducts of said steps (c4), (c5), and (c6) to produce a filtered pixelof image data.
 28. The method as claimed in claim 27, wherein said step(a) includes the substeps of:(a1) calculating a summation of absolutevalues of Laplacians of a window of pixels around the pixel to beclassified; (a2) determining a grey level range within the window ofpixels; (a3) determining a two-dimensional frequency around the pixel beclassified; (a4) determining a percentage of pixels in the window havinga grey value less than a predetermined black threshold; and (a5)determining a percentage of pixels in the window having a grey valuegreater than a predetermined white threshold.
 29. The method as claimedin claim 28, wherein said step (a) further includes the substeps of:(a6)determining a conditional value of the pixel with respect to a conditionof the summation of said substep (a1) being greater than a firstpredetermined value, determining a conditional value of the pixel withrespect to a condition of the determined range of said substep (a2)being greater than a second predetermined value, and determining aconditional value of the pixel with respect to a condition of thedetermined frequency of said substep (a3) being greater than a thirdpredetermined value; (a7) establishing a conditional value for the pixelwith respect to a halftone class by determining which conditional valuedetermined in said substep (a6) is a minimum value and assigning theminimum value as the conditional value; (a8) determining a conditionalvalue of the pixel with respect to a condition of the summation of saidsubstep (a1) being greater than a fourth predetermined value,determining a conditional value of the pixel with respect to a conditionof the determined range of said substep (a2) being greater than a fifthpredetermined value, and determining a conditional value of the pixelwith respect to a condition of the determined frequency of said substep(a3) being greater than the third predetermined value; (a9) establishinga membership value for the pixel with respect to an edge class bydetermining which conditional value determined in said substep (a8) is aminimum value and assigning the minimum value as the membership value;(a10) determining a conditional value of the pixel with respect to acondition of the summation of said substep (a1) being less than thefirst predetermined value and determining a conditional value of thepixel with respect to a condition of the determined frequency of saidsubstep (a3) being less than the third predetermined value; and (a11)establishing a conditional value for the pixel with respect to a contoneclass by determining which conditional value determined in said substep(a10) is a minimum value and assigning the minimum value as theconditional value.
 30. The method as claimed in claim 29, wherein saidstep (a) further includes the substeps of:(a12) determining aconditional value of the pixel with respect to a condition of thepercentage determined in said substep (a4) being greater than a sixthpredetermined value; (a13) establishing a membership value for the pixelwith respect to a black class by determining which conditional valuedetermined in said substeps (a11) and (a12) is a minimum value andassigning the minimum value as the membership value; (a14) determining aconditional value of the pixel with respect to a condition of thepercentage determined in said substep (a5) being greater than a seventhpredetermined value; (a15) establishing a membership value for the pixelwith respect to a white class by determining which conditional valuedetermined in said substeps (a11) and (a14)is a minimum value andassigning the minimum value as the membership value; (a16) determining aconditional value of the pixel with respect to a condition of thepercentage determined in said substep (a4) being less than the sixthpredetermined value and determining a conditional value of the pixelwith respect to a condition of the percentage determined in said substep(a5) being less than the seventh predetermined value; and (a17)establishing a membership value for the pixel with respect to apictorial class by determining which conditional value determined insaid substeps (a11) and (a16) is a minimum value and assigning theminimum value as the membership value.
 31. The method as claimed inclaim 30, wherein said step (a) further includes the substeps of:(a18)determining a conditional value of the pixel with respect to a conditionof the frequency determined in said substep (a3) being less than aneighth predetermined value; (a19) establishing a membership value forthe pixel with respect to a low frequency halftone class by determiningwhich conditional value determined in said substeps (a7) and (a18) is aminimum value and assigning the minimum value as the membership value;(a20) determining a conditional value of the pixel with respect to acondition of the frequency determined in said substep (a3) being greaterthan the eighth predetermined value and determining a conditional valueof the pixel with respect to a condition of the frequency determined insaid substep (a3) being less than a ninth predetermined value; (a21)establishing a membership value for the pixel with respect to a midfrequency halftone class by determining which conditional valuedetermined in said substeps (a7) and (a20) is a minimum value andassigning the minimum value as the membership value; (a22) determining aconditional value of the pixel with respect to a condition of thefrequency determined in said substep (a3) being greater than the ninthpredetermined value; and (a23) establishing a membership value for thepixel with respect to a high frequency halftone class by determiningwhich conditional value determined in said substeps (a7) and (a22) is aminimum value and assigning the minimum value as the membership value.32. The method as claimed in claim 31, wherein said step (a) furtherincludes the substep of:(a24) establishing a membership value for thepixel with respect to the other class by determining which membershipvalue determined in said substeps (a9), (a13), (a15), (a17), (a19),(a21), and (a23) is a maximum value and assigning a value equal to oneminus the maximum value as the membership value for the other.
 33. Asystem for electronically screening a pixel belonging to a set ofdigital image data with respect to a membership of the pixel in aplurality of image classes and an other class, comprising:fuzzyclassifying means for determining a membership value for the pixel foreach image class and a membership value for the pixel for the otherclass, each image class being defined by a set of heuristic rules, thesets being non-mutually exclusive, the membership value for an imageclass being independent of the other membership values for the imageclasses, the membership value for the other class being dependent on themembership values of the image classes; effect means for generating aclassification vector for the pixel based on the determination by saidfuzzy classifying means; and screening means screening the pixel basedon the membership values in the classification vector associated withthe pixel.
 34. The system as claimed in claim 33, wherein said screeningmeans includes:means for generating a screen value according to aposition of the pixel in the set of digital image data; means forgenerating a screen amplitude weighting value based on the membershipvalues in the classification vector for the pixel; means for multiplyingthe screen value and the screen amplitude weighting value to produce amodified screen value; and means for adding the modified screen value tothe pixel of image data.
 35. A system for electronically filtering apixel belonging to a set of digital image data with respect to amembership of the pixel in a plurality of image classes and an otherclass, comprising:fuzzy classifying means for determining a membershipvalue for the pixel for each image class and a membership value for thepixel for the other class, each image class being defined by a set ofheuristic rules, the sets being non-mutually exclusive, the membershipvalue for an image class being independent of the other membershipvalues for the image classes, the membership value for the other classbeing dependent on the membership values of the image classes; effectmeans for generating a classification vector for the pixel based on thedetermination by said fuzzy classifying means; and filter means fortwo-dimensionally filtering the pixel based on the membership values inthe classification vector associated with the pixel.
 36. The method asclaimed in claim 35, wherein said filter means includes:a lowpassfilter; a highpass filter; a bypass filter; a first multiplier tomultiply the lowpass filtered pixel by a gain factor based on themembership values in the classification vector associated with thepixel; a second multiplier to multiply the highpass filtered pixel by again factor based on the membership values in the classification vectorassociated with the pixel; a third multiplier to multiply the bypassfiltered pixel by a gain factor based the membership values in theclassification vector associated with the pixel; and an adder to add theproducts of said first, second, and third multipliers to produce afiltered pixel of image data.